Mohamed El

Yazid Boudaren
email: boudaren@gmail.com

Abdel Belaïd
email: abelaid@loria.fr

Extended Dependency Tree-HMM for Non-Rectangular Sub-Images Modeling

Keywords: Hidden Markov Models (HMM), Dependency Tree-HMM, Sub-Image Modeling. --------

This work is motivated by the need of evaluating the likelihood probability on sub-images of not necessarily rectangular shape in some frameworks. For this purpose, we propose an alternative of Dependency Tree-HMM that allows the four traditional interactions between neighboring pixels instead of just two. To demonstrate the accuracy of the proposed model, we provide some classification results performed on high resolution aerial images.

Introduction

Pixel-wise approaches for image classification are usually not suitable to solve problems often encountered in remote sensing applications [START_REF] Mesev | Remotely Sensed Cities[END_REF][START_REF] Thomas | A comparison of urban mapping methods using high-resolution digital imagery[END_REF]. They result in a disgusting salt and pepper effect. To overcome the drawback of such approaches, more elaborated methods classify each pixel by taking into account some of its neighboring pixels, usually by computing a similarity measure ( likelihood probability for instance) [START_REF] Permuter | Gaussian mixture models for texture and colour for image database retrievel[END_REF]. In most cases, this computation concerns pixels contained in square windows centered at the pixel to classify. However, in some stochastic image modeling frameworks, one may have to evaluate likelihood probabilities on nonrectangular windows, like in [START_REF] Boudaren | Combining Extended Dependency Tree-HMM based Recognition and Unsupervised Segmentation for Land Cover Mapping in Aerial Images[END_REF]. In this work, we propose to extend the so-called dependency treehidden Markov model (DT-HMM) proposed in [START_REF] Merialdo | Dependency Tree Hidden Markov Models[END_REF] to make possible such computation by allowing the four directional interactions between neighboring pixels instead of just two. This allows one to approximate the genuine 2D-HMM likelihood probability of observing the data contained in circular-like shaped windows or image blocks while maintaining the linear complexity of the traditional 1D-HMM. It was proven in some previous research works [START_REF] Merialdo | A New Approach to Probabilistic Image Modeling with Multidimensional Hidden Markov Models, Adaptive multimedia retrieval[END_REF] that the average value of the likelihood probability computed according to a relatively small number of random dependency trees constitutes a good estimate of the authentic likelihood probability.

The reminder of the paper is organized as follows: section 2 summarizes some previous works where likelihood probability was evaluated on square windows, and shows up the importance of adopting sub-images of different shapes instead of square ones. Section 3 gives an overview about the classical DT-HMM and introduces the EDT-HMM. To validate our theoretical formalism, we show some results obtained on real world high resolution aerial images using the classification scheme proposed in [START_REF] Boudaren | A New Scheme for Land Cover Classification in Aerial Images[END_REF]. Future improvements and conclusion remarks are given in section 5.

Evaluation Windows for Image Modeling

Achieving a statistical classification at pixel level is a difficult ill-posed problem in pattern recognition; most methods evaluate likelihood probability over square windows of the same size [START_REF] Permuter | Gaussian mixture models for texture and colour for image database retrievel[END_REF], which is chosen experimentally. Two particular examples of image classification at pixel level would be fabric defects detection or aerial image indexing. Such problems do not just require the segmentation of the image into distinctive regions which may be performed by unsupervised algorithms such as EDISON [START_REF] Comaniciu | Mean shift: A robust approach towards feature space analysis[END_REF][START_REF] Meer | Edge detection with embedded confidence[END_REF], but the identification of image pixels. Obviously, such a classification subsumes segmentation but it does not lead necessarily to a segmentation of the whole image. In this scope, unsupervised algorithms are known to be more suitable to segment an image into regions. However, supervised algorithms are required to achieve the identification. In fact, good classifiers apply a filtering and/or segmentation as pre-processing step before carrying out the supervised classification. Such classifiers affect then each pixel to its appropriate class after evaluating likelihood probability on a square window centered at that pixel. Adopting windows aims to take account of contextual information or/and some texture characteristics that cannot be derived from a lonely pixel. The main issue of such a classification scheme is how to pick an appropriate window size; in general, the bigger is the window, the more accurate is the decision. However, a window may be heterogeneous since it may include pixels belonging to more than one class. To take advantage of the preprocessing step (segmentation or/and filtering), one has to restrict window pixels to those belonging to the same class than that of the pixel to identify.

In [START_REF] Garcia | Supervised texture classification by integration of multiple texture methods and evaluation windows[END_REF], the authors combine various texture classification methods over multiple square windows of different sizes to detect fabric defects. Their classifier integrates various texture features computed on square windows. In [START_REF] Permuter | Gaussian mixture models for texture and colour for image database retrievel[END_REF], texture image is divided into rectangular blocks of the same size and affect pixels of a same block to the same class.

A wide variety of texture feature extraction methods have been proposed in the computer vision literature [START_REF] Novianto | Multiwindowed approach to the optimum estimation of the local fractal dimension for natural image segmentation[END_REF]. Their performance depends basically on the processing they apply, the neighborhood of pixels over which they are evaluated (window size and shape) and the texture features. When dealing with these methods, one big issue must be addressed: the determination of the window size and shape. Although many studies regarding the performance of the different families of texture extraction methods, only few dealt with the issue of determining the optimal window size [START_REF] Blostein | Shape from texture: integrating textureelement surface estimation[END_REF][START_REF] Cohen | The segmentation of images of unknown scale using multi-scale texture tuned masks[END_REF][START_REF] Novianto | Multiwindowed approach to the optimum estimation of the local fractal dimension for natural image segmentation[END_REF]. The influence of the window size and shape on classifiers performance was studied in [START_REF] Garcia-Sevilla | Analysis of irregularly shaped texture regions: a comparative study[END_REF] where it has been shown that texture characterization is influenced much more by window size than its shape. Commonly, the window size is defined experimentally depending on the method.

In [START_REF] Boudaren | A New Scheme for Land Cover Classification in Aerial Images[END_REF], we proposed a new scheme for land cover classification that advantageously combines an unsupervised segmentation and a statistical recognition. From segmentation results, we suggested to classify each pixel by computing likelihood probability over a particular neighborhood containing pixels surrounding the one to be identified and belonging to its same region. Unlike [START_REF] Permuter | Gaussian mixture models for texture and colour for image database retrievel[END_REF], adopting rectangular windows is not affordable here. Indeed, taking account of heterogeneous pixels within the same window would definitely introduce a bias in both learning and retrieval processes and falsify then the indexing.

Dependency Tree-HMM Extension

Markov models (Markov Random Fields, Hidden Markov Fields, Hidden Markov Models, Hidden Markov Trees…) were extensively and successfully used for texture modeling and segmentation [START_REF] Noda | MRF based texture segmentation using wavelet decomposed images[END_REF]. This is majorly due to their ability to model contextual dependencies and noise absorption [START_REF] Pieczynski | Markov models in image processing[END_REF]. However, their performance depends widely on the model architecture: genuine 2D-models yield better results but exhibits much higher computational complexity [START_REF] Pieczynski | Markov models in image processing[END_REF]. In general, the more complex is the model, the better are the performances.

Nevertheless, for computational complexity reasons, several approaches consider linear models like HMM even if such a model is not suited for twodimensional data [START_REF] Boudaren | Hidden Markov model based classification of natural objects in aerial pictures[END_REF]. More elaborated approaches resort to 2D-models with simplifying assumption. One simplifying assumption that provides good results with a linear complexity is that assumed in DT-HMM [START_REF] Merialdo | Dependency Tree Hidden Markov Models[END_REF][3]: one site (image pixel) may depend on either the horizontal or vertical predecessor, but not on both the same time.

The extension of DT-HMM in this work is motivated by two reasons:

The need to compute likelihood probability on non-rectangular shaped windows of different sizes.

The need to adopt central pixel (to be labeled) as the dependency tree root since the root shows more interactions with neighbors than other pixels do.

Before describing our model principles, let us define the applicability conditions of the EDT-HMM model on a window w with respect to root r .

The window w must fit the following condition: Let w be a window verifying the condition above, and let r be the center of the window and { }

w j i s y Y s r ∈ = ) , (
be the set of features vectors (RGB for instance) of pixels inside w . r Y is then the observable process. Let X be the hidden process.

The likelihood probability is given by:

( ) ( ) ( ) ) 1 ( , λ λ λ X P X Y P Y P X r r ∑ =
Unlike DT-HMM, where each pixel may have a predecessor chosen between two directions, in the EDT-HMM modeling, a pixel s may have a predecessor v chosen randomly from the 4- Neighborhood (up, down, right or left) and verifying the Euclidean distance property. Note that, the neighborhood directions of all pixels of w define a tree structure T like depicted in figure 1. We note

( ) v s T = .
The likelihood probability to observe r Y given the parameters of the DT-HMM ( ) B A, , π λ can be approximated as follows:

( ) ( ) ( ) ( ) 2 , , ∑ ∑ ∏       ≈ ∈ T X w s s s s r T x P x y P Y P λ λ λ
In this paper, we propose to evaluate the likelihood on a set of random dependency treesτ . The previous equation becomes: This computation exhibits a reasonable complexity (linear with window size).

( ) ( ) ( ) ) 3 ( , , ∑∑ ∏ ∈ ∈       ≈ τ λ λ λ
The extension of the DT-HMM only concerns likelihood probability computation and Viterbi decoding whereas learning is performed the same way as in DT-HMM context.

The Viterbi decoding process can be achieved in a similar way to the likelihood probability computation. In this work, we only resort to likelihood probability computation.

On the other hand, learning is performed via an iterative way the same as for the DT-HMM model, since the parameters are the same: Initialize model parameters.

Choose a random dependency tree T as described above (respecting the Euclidean distance constraint).

Perform learning as in a linear framework (like in 1D-HMM).

Experiments

To validate the theoretical formalism proposed in the previous section, we provide here some experimental results obtained on real world high resolution images from RGD74-75 database [START_REF]Régie de Gestion des Données des Deux Savoies[END_REF] (Fig. 1) using the classification scheme proposed in [START_REF] Boudaren | A New Scheme for Land Cover Classification in Aerial Images[END_REF]. Our objective is to locate snow and vegetation in the images. As preprocessing, we segmented each aerial image into homogenous regions using EDISON software. Then, we identify each pixel by computing likelihood probability over circular-like windows that fit the applicability conditions of the proposed EDT-HMM. This allows us to perform the computing simultaneously on different machines. Finally, we corrected the indexing by merging the pixels belonging to the same region by affecting them to the predominating class (Fig. 2).

Conclusion

Most pixel-based classifiers adopt rectangular windows to identify image pixels. To advantageously take account of pre-processing steps like filtering and segmentation, one should exclude some neighboring pixels from window patches. Since current statistical algorithms are not appropriate to evaluate likelihood probability on such sub-images, we proposed an alternative of DT-HMM that makes such a computation affordable within a linear computational complexity. Moreover, our approach gives one the opportunity to perform the classification process on multi-processor mode; this considerably reduces the processing time. The results performed on real world high resolution aerial images, presented in this paper confirm the validity of our modeling.

For each site s of w , s must have at

  of observing the data contained in the sub-tree of T with s as a root starting from the hidden state i .

  Note that the likelihood probability of equation 3 can be evaluated as follows for each dependency tree T :

Figure 1 :

 1 Figure 1: Examples of aerial images from RGD 73-74

Figure 2 :

 2 Figure 2: Classification results (vegetation in green and snow in white)


I Ben Cheikh

A Kacem1

A Belaïd
email: abelaid@loria.fr

A Neural-Linguistic Approach for the Recognition of a Wide Arabic Word Lexicon

Keywords: Arabic writing recognition, wide vocabulary, neural networks, linguistic knowledge integration, canonic vocabulary

Recently, we have investigated the use of Arabic linguistic knowledge to improve the recognition of wide Arabic word lexicon. A neural-linguistic approach was proposed to mainly deal with canonical vocabulary of decomposable words derived from tri-consonant healthy roots. The basic idea is to factorize words by their roots and schemes. In this direction, we conceived two neural networks TNN_R and TNN_S to respectively recognize roots and schemes from structural primitives of words. The proposal approach achieved promising results. In this paper, we will focus on how to reach better results in terms of accuracy and recognition rate. Current improvements concern especially the training stage. It is about 1) to benefit from word letters order 2) to consider "sisters letters" (having same features), 3) to supervise networks behaviours, 4) to split up neurons to save letter occurrences and 5) to solve observed ambiguities. Considering theses improvements, experiments carried on 1500 sized vocabulary show a significant enhancement: TNN_R (resp. TNN_S) top4 has gone up from 77% to 85.8% (resp. from 65% to 97.9%). Enlarging the vocabulary from 1000 to 1700 by 100 words, again confirmed the results without altering the networks stability.

INTRODUCTION

Many researches have been undertaken in the field of typed and handwritten Latin, Chinese and Japanese writing recognition. Little progress, however, has been made in Arabic writing due to its cursive nature. Besides, several approaches (holistic, pseudo-global and analytic) have been proposed according to vocabulary size (reduced, slight extended or wide).

The recognition of a wide vocabulary of Arabic words poses challenges from not only the characteristics of Arabic language, but also the limits of existing approaches and paucity of training data available. Because of Arabic morphological complexity, effective Arabic words go past 60 billions [START_REF] Cheriet | Visual Processing of Arabic Handwriting: Challenges and New Directions[END_REF] what makes their automatic processing unrealistic and constitutes handicaps for dictionary building, automatic spelling... To remedy such problem, reducing this number becomes mandatory. To this end, words morphological analysis and factorization seem to be one solution.

Current trends are for incorporating linguistic knowledge, at various levels (lexical, syntactic and semantic), either keeping them as an independent stage, or integrating them partly or totally in the recognition chain [START_REF] Cheriet | Visual Processing of Arabic Handwriting: Challenges and New Directions[END_REF]. In this context, we proposed, in a previous work [START_REF] Ben Cheikh | A Novel Approach for the Recognition of a Wide Arabic Handwritten Word Lexicon[END_REF], a neural-linguistic approach based on two neural networks: TNN_R and TNN_S to respectively recognize the root, from which the word derives, and the scheme, the word follows, by the use of linguistic knowledge. Carried experiments demonstrated that results are promising. Now, we will mainly focus on how to improve the approach in order 1) to achieve better results, in terms of accuracy and recognition rate and 2) to handle with more and more large vocabularies.

In section 2, we will illustrate how the Arabic linguistic knowledge was considered in writing recognition in previous related research works. Next, we will remind the concepts underlying our approach. Afterwards, we will explain our improvement strategy. By conducting experiments, we will finally demonstrate the networks stability, face to wider and wider vocabularies, and investigate their shortcomings to plan for further improvements as future work.

ARABIC LINGUISTIC KNOWLEDGE INTEGRATION

Many studies highlight the richness and the stability of Arabic in terms of morpho-phonologic peculiar to this language [START_REF] Cheriet | Visual Processing of Arabic Handwriting: Challenges and New Directions[END_REF][START_REF] Hamadou | Vérification et Correction Automatiques par Analyse Affixale des Textes Ecrits en Langage Naturel[END_REF][START_REF] Kanoun | Affixal Approach for Arabic Decomposable Vocabulary Recognition: A Validation on Printed Word in Only One Font[END_REF][START_REF] Kammoun | Reconnaissance de Textes Arabes à Vocabulaire Ouvert[END_REF]. An Arabic word is decomposable or not. If it derives from a root, it is said to be decomposable in morphemes (root, prefix, infix and suffix). A word is, then, composed of root letters and access (non-root) letters. In [START_REF] Cheriet | Visual Processing of Arabic Handwriting: Challenges and New Directions[END_REF], Cheriet proposed to exploit this word vision using any recognition approach. He suggested analysing errors using linguistic clues and in rejection cases, extracting the root and doing template matching to infer the rest. He affirmed that one must focus on what kind of linguistic knowledge is important and how and where it is more appropriate to incorporate. By adopting the same vision of the word, an "affixal approach", proposed by [START_REF] Kanoun | Affixal Approach for Arabic Decomposable Vocabulary Recognition: A Validation on Printed Word in Only One Font[END_REF] then reconsidered by [START_REF] Kammoun | Reconnaissance de Textes Arabes à Vocabulaire Ouvert[END_REF] for the recognition of Arabic typed texts, consists in the segmentation of words in letters and the recognition of their morphological entities. The authors used several linguistic concepts (Affixal and semantic restrictions) to guide the recognition process.

Our vision of the word is slightly different. In fact, we consider a decomposable word as being the derivation of its root according to a conjugated scheme. This latter is the association of prefix and suffix (letters from the conjugation: time, kind, number...) to a brief scheme. The derivation of the root with the brief scheme gives rise to the radical (see Fig. 1). Prefix and suffix are composed of access letters corresponding to the conjugation, while other access letters belong to the radical and depend on the scheme. Thus, according to our vision, to recognize a word, we just need to identify its root and conjugated scheme (conjugation elements), without segmenting it in letters. This is the main idea which will allow us to factorize words and to handle with a wide vocabulary while using a holistic approach to avoid effective segmentations.

NEURAL-LINGUISTIC APPROACH

In [START_REF] Ben Cheikh | A Novel Approach for the Recognition of a Wide Arabic Handwritten Word Lexicon[END_REF], we proposed an approach for the recognition of a wide Arabic canonical vocabulary. The idea is to integrate linguistic knowledge to a perceptive model by the use of transparent neural networks (TNN), having as input global primitives of words. Such model allows not only to avoid segmentation, due to cursive nature of Arabic, but also to fasten result analysis and error correction since neurons are no longer black boxes; they are rather associated with concepts. It is important to notice that 1) we have already experimented the use of TNN on a reduced vocabulary as reported in [START_REF] Cheikh | Neural Network for the Recognition of Handwritten Tunisian City Names[END_REF] and 2) the considered system was inspired from a human vision system developed, first, by Mc. Culloch, used then for Latin by M. Côté, and reconsidered for Arabic by S. Maddouri [START_REF] Maddouri | Local Normalization Towards Global Recognition of Arabic Handwritten Script[END_REF] operating, first, globally by extracting the most obvious primitives, then while refining its vision following several perceptive cycles.

Within this approach, the recognition is founded on two transparent neural networks TNN_R and TNN_S, having as input the word structural primitives, but TNN_R tries to provide its root when TNN_S tries to supply its scheme. Dealing with large vocabulary of 8000 words for example (derived from 100 roots using different schemes and conjugations), we have succeeded in reducing necessary neurons from 8000 to 180 (100 neurons for roots + 80 neurons for schemes and conjugation Radical elements) by adopting the following architectures for TNN_R and TNN_S (see Fig. 2). TNN_R is a three-layer network which learns how to focus on root letters and ignores access ones, reserved for schemes, while TNN_S is a four-layer network which learns how to ignore root letters and focus on schemes ones. Note that dissociating roots from schemes was necessary since these two entities do not require the same information to be learned. In fact, word PAWs (Piece of Arabic Word) are useless for the training of its root (e.g. PAWs of both words " آ ﺎ ﻓﺢ " and " آﻔ ﺎ ح " correspond to the root )"آﻔﺢ" but useful for the training of its scheme (e.g. PAWs of " آﺎ ﻓﺢ " lead to the scheme " ﻓﺎ ﻋﻞ " whereas PAWs of " آﻔﺎ ح " match the scheme " ﻓﻌﺎ ل ").

For the training of TNN_R and TNN_S, which are transparent networks, instead of adjusting their connections weights manually, we have chosen to dissociate them into mono-layer-Perceptrons to make their automatic training possible. Previous experimentations, carried on 1500 sized vocabulary are justly considered as a proof of the concept. Reached results were encouraging but not satisfying enough (top4 for TNN_R and TNN_S are resp. equal to 77% and 65%). Next, we will explain our improvement strategy.

NEURAL-LINGUISTIC APPROACH IMPROVEMENT

The improvement of our approach mainly concerns the training stage:  Exploiting word letters order information,  Introducing the "sisters letters" concept,  Supervising TNN_R and TNN_S behaviours,  Splitting up of neurons, and  Solving ambiguities during the training stage.

The improvement might be extended to the recognition stage in order to handle collisions using "Perceptive cycles" and "Linguistic constraints".

Masculine 4.1. Letters order information

Since neural networks have great learning capacities, we think to better exploit network inputs by the use of values in the interval [0,1] instead of only 0 or 1. These values refer to possible letter positions in the word. As shown in Fig. 3, to learn the root "ﺻﺮف" of the word ,"اﻧﺼﺮف" the activation of the letter " ﺻ ﺮ ", will be 0.33 as it refers to the third position.

"Sisters letters" concept

Sisters of a letter are those which are described by the same primitive. For example, " ﺻ ﺮ ", " ﻣ ﻲ " and " ه ﻲ " are sisters letters, since all of them present the same primitive : "LM": Loop in the Middle. As shown in Fig. 3, to learn the root "ﺻﺮف" of the word ,"اﻧﺼﺮف" we activate, in inputs layer, not only letters composing the word but also sisters of each letter (see dotted circles). This avoids sisters letters to be concurrent in the root training. Of course, sisters of one letter have the same activation as the letter itself.

TNN_R and TNN-S supervising

Since TNN_R is conceived to focus on root letters and ignore access ones, we will inhibit neurons of letters which can never be root letter. Oppositely, for TNN_S, we will inhibit neurons of letters which can never be access letter. For example, as illustrated in Fig. 4, in the training of the scheme ,"ﺗﻔﺎﻋﻞ" composed of " ﺗ ﺎ ? ﺗ ﺎ " and "??" ("?" refers to a root letter), we will inhibit the root letters " ڍ ﻗ ﮃ " and " ﺗ ﻌ ﺎ ". Note that the letter " ﻧ ڊ ", which is here a root letter, is not deactivated since it could be a scheme letter like in the schemes " اﻧﻔ ﻌﻞ ", "ﻣﻨﻔﻌﻞ" and ."اﻧﻔﻌﺎل" Note that the use of letters order, sisters letters and networks supervising contributes to considerably increase the recognition rates especially for TNN_R (top4 from 77% to 85.8% with top1=76.1%). For TNN_S, the obtained results have also grown but not enough (top4 from 65% to 76.5% with top1=35%). That incites us to look for further improvements for TNN_S.

Neurons splitting up

Observing TNN_S recognition behaviour face to miss-recognized words, we find that the last occurrence of a letter overwrites the previous ones. For example, in the word "ﺣﺎزﻣﺎن" which matches the scheme ﻓﺎﻋﻞ" ", the letter " ﺗ ﺎ " occurs twice: 1) as a scheme letter (2nd position) and 2) as a conjugation letter (5th position). The first occurrence gets over by the second one which will set the activation of " ﺗ ﺎ " to 0.55. Therefore, we lose a determining letter (" ﺗ ﺎ " activated to 0.22) of the scheme ﻓﺎﻋﻞ" ". By the same way, the last occurrence of a scheme letter, in a word, overwrites the previous ones. For example, in the word "اﻗﺘﺮاب" which corresponds to the scheme " اﻓﺘﻌ ﺎل ", the letter "ا" is, in both positions, a scheme letter.

To address this problem, we believe in the neurons splitting up. It is about to split up each scheme letter neuron of TNN_S into a set of neurons. The number of split neurons depends on the scheme letter. For example, " ﺗ ﺎ " has five possible determinant positions in a word, thus it will be split into five neurons, while " ﺗ ﺎ " has only two significant positions (1st and 3rd), then only two split neurons. Note that scheme letter neurons splitting up permitted to enhance the results: top4 from 76.5% to 95% (top1 from 35% to 68%).

Ambiguities solving at the training stage

Through TNN_S transparency, we realized that some schemes' letters vote competitively when they have the same position in the word. This led to cases of confusion. For instance, if the schemes' letters " ﺗ ﺎ " and " ﻧ ڊ ", which have the primitive "DaM" (Diacritics above in the Middle), are in the second position (e.g. in the words " ﻳ ﺘ ﺤﺎرب " and ,)"ﻳﻨﺼﺮف" they will vote, simultaneously and respectively, to the schemes ﺗﻔﺎﻋﻞ" " and ."اﻧﻔﻌﻞ" Therefore, their contributions weaken what allows other neurons to take over and cause collisions. Further improvement for TNN_S training is, then, necessary. This is to ensure that these letters vote exclusively, since two different letters cannot be in the same position of the same word. For that, we will take advantage from some schemes' features to decide which one will exclude the other. For example, when learning the scheme ﺗﻔﺎﻋﻞ" " from the word ,"ﻳﺘﺤﺎرب" the letter " ﺗ ﺎ " will exclude the letter " ﻧ ڊ ", thanks to the presence of the scheme letter " ﺗ ﺎ ". This improvement has enabled us to reach a top1 equal to 95.4% for TNN_S.

EXPERIMENTATIONS

Considering the above proposed improvements, we conducted experimentations aiming 1) the survey of the behaviour of the model with regard to more and more large vocabularies and 2) the visual inspection of the networks, for a given vocabulary, to identify their weaknesses and to act well accordingly.

Model behaviour face to vocabulary size

To observe TNN_R and TNN_S behaviours, we used 8 different sized vocabularies ranging from 1000 to 1700 by injecting 100 new words each time. We executed the recognitions using test corpus specific to each vocabulary.

As shown in Fig. 5(b), TNN_R behaves in a stable manner with the growth of the vocabulary size. Note that a good top4 is sufficient, since we plan to apply "perceptive cycles" to get the right root among the first 4 candidates.

The more the vocabulary is large, the better TNN_S learns as illustrated by Fig. 5 (b). This result was expected since the schemes number is fixed. It is important to note that the training of a 1700 sized vocabulary gives better results than the 1600 sized one.

Model visual inspection

Considering a vocabulary with a given size (for instance 1700), it is about to investigate TNN_R and TNN_S limits.

TNN_R Investigation:

We noted that TNN_R does not manage to learn the roots of some words. Observing some collisions, we found that they are due to root letters which are described by the same primitive, as illustrated in Fig. 6. This figure shows two examples (a,b) of words in which, one primitive could make appear many letters (see red continue circles and arrows). Thus, for one word, more than one root could be candidates (see green and blue dotted arrows and squares). TNN_S Investigation : By the same way, we concluded that some schemes have not been well learned. As explained in Fig. 7, we found that many ambiguities arise from letters which are, at once, root and access letters. This figure gives an example of a word which can be seen in two different views because of letters which are root and access letters at once. Consequently, two schemes are concurrent: according to view1, green continue encircled letters contribute to a scheme whereas, in view2, blue dotted ones contribute to another.

CONCLUSION AND FUTURE WORK

This work shows that giving slightly higher weight to linguistic information offers not only better results, but also solutions to handle wider vocabulary. In comparison with our previous work [START_REF] Ben Cheikh | A Novel Approach for the Recognition of a Wide Arabic Handwritten Word Lexicon[END_REF], the taking into account of the above improvements (letters order, "sister letters", neurons splitting up, networks supervising and ambiguity solving) has proven to significantly help the model reach its classification goal. Results are provided and show that, even without considering yet the suggested improvement proper to the recognition stage (perceptive cycles and linguistic constraints), our approach always produces higher success rates than the current approaches with regard to the vocabulary size as illustrated in table 1. Note that we are comparing our pseudo-global "neural-linguistic" approach to other ones which 1) use linguistic knowledge like [START_REF] Kanoun | Affixal Approach for Arabic Decomposable Vocabulary Recognition: A Validation on Printed Word in Only One Font[END_REF] and [START_REF] Kammoun | Reconnaissance de Textes Arabes à Vocabulaire Ouvert[END_REF] and/or 2) are conceived for large vocabularies like [START_REF] Touj | A Hybrid Approach for Off-line Arabic Handwriting Recognition Based on a Planar Hidden Markov Modeling[END_REF], even though the experimentation lexicon of some of them is not wide enough.

To demonstrate the robustness of our approach face to wider vocabulary, we tested it on different sized lexicons (from 1000 to 1700) and succeeded to prove that the model maintains a stable behavior. These experiments confirm neural-linguistic approach to be well suited for the recognition of a wide vocabulary of Arabic words. For the recognition stage, we believe that "perceptive cycles" and "linguistic constraints" should bring a distinct improvement of the current results.

Fig. 1 .

 1 Fig. 1. Our Arabic word vision.

Fig. 2 .

 2 Fig. 2. (a): TNN_R Architecture, (b): TNN_S Architecture

Fig. 3 .

 3 Fig. 3. The use of letters order and sisters letters

Fig. 4 .

 4 Fig. 4. TNN_S supervising

Fig. 5 .

 5 Fig. 5. TNN_R (a) and TNN_S (b) behaviors face to vocabulary size

Fig. 6 .

 6 Fig. 6. Root collisions due to similar letters.

Fig. 7 .

 7 Fig. 7. Scheme collisions due to root/access letters.

Table 1 .

 1 Proposed approach vs. current approaches

	Approach	Writing	Vocab. Size Top1 Top2 Top3 Top4
	Analytic [4]	Typed	1000	74	81.2	83.9	85
	Analytic [5]	Typed	1423	81.3 95.7	96.4	99.7
	Analytic [8]	Handwritten	25	88,7	-	-	-
	Pseudo-global (our approach)	Typed	TNN_R 77.5 83.8 1700 TNN_S 95 97.3	86 97.8	87.6 98.7


Mohamed El

Yazid Boudaren
email: boudaren@gmail.com

Abdel Belaïd
email: abelaid@loria.fr

M E Y Boudaren

A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

Keywords:

An important challenge to any image pixels classification system is to correctly assign each pixel to its proper class without blurring edges delimiting neighboring regions.

In this paper, we present an aerial image mapping approach that advantageously combines unsupervised segmentation with a supervised Markov model based recognition. The originality of the proposed system carries on three concepts: the introduction of an auto-adaptive circular-like window size while applying our stochastic classification to preserve region edges, the extension of the Dependency Tree-HMM to permit the computation of likelihood probability on windows of different shapes and sizes and a mechanism that checks the coherence of the indexing by integrating both segmentations results: from unsupervised over segmentation, regions are assigned to the predominating class with a focus on inner region pixels. To validate our approach, we achieved experiments on real world high resolution aerial images. The obtained results outperform those obtained by supervised classification alone.

Introduction

Land Cover Classification (LCC) in high resolution aerial images is an important application of remote sensed data. It consists of identifying the natural objects present in a high resolution aerial image given a set of known patterns. In the most general case of aerial images, when the image contains several regions of different patterns, the aim is to label each pixel with the corresponding texture. Evidently, the labeling process subsumes image segmentation but besides segmenting the image to different regions, it assigns each region to one of the natural objects patterns.

Achieving the classification at pixel level is a big issue in LCC problem. In fact, it is easier to identify an image of a relatively big size than identifying a lonely pixel. In fact, pixel-wise approaches for image classification are not usually suitable to solve problems often found in remote sensing application [START_REF] Mesev | Remotely Sensed Cities[END_REF][START_REF] Thomas | A comparison of urban mapping methods using high-resolution digital imagery[END_REF]. They result in a disgusting salt and pepper effect.

Recent researches clearly show the advantages of integrating spatial dimension to spectral features by using segmentation based classification methods and, hence, focusing into image regions instead of pixels [START_REF] De | Remote Sensing ImageAnalysis: Including the Spatial Domain[END_REF][START_REF] Jensen | Introductory Digital Image Processing[END_REF].

More elaborated approaches use a family of Markov models to model the contextual interactions between labels. However, genuine 2D-Markov modeling of the contextual information is a time consuming iterative process [START_REF] Levin | Dynamic planar warping for optical character recognition[END_REF].

On the other hand, reasonable complexity approaches identify each pixel by taking into account its neighboring pixels, usually by computing a similarity measure (likelihood probability for instance) on square windows centered at concerned pixels [START_REF] Permuter | Gaussian mixture models for texture and colour for image database retrieval[END_REF]. The drawbacks of such approaches are the following:

They adopt square windows which may introduce a bias toward rectangular regions. Moreover, corner pixels are more distant than other pixels. Adopting non-square windows is usually unaffordable due to the used model or measure nature. The bigger is the window, the more likely the identification is correct. However, adopting a too big window may penalize small regions. A tradeoff is generally made. Since a static window size is adopted, the window size is then too small to perform efficient classification for all image pixels and too high to preserve edges since the classification of frontier pixels are biased through introduction of neighborhood pixels.

In this paper, we propose a system that overcomes the previous difficulties by introducing the following: Segmentation is achieved through unsupervised segmentation which preserve region edges even if it provides an over-segmented image. Each region is identified through stochastic supervised classification. Likelihood probability may be computed on windows of different sizes and shapes centered at considered pixels. To determine window size and shape, an auto-adaptive distance is computed based on the considered pixel position towards region edges. To permit likelihood probability computation on non-rectangular windows, we extended the Dependency Tree-hidden Markov model (DT-HMM) by allowing four directional dependencies instead of two, and adopting the central pixel as root instead of upper-left pixel when dealing with rectangular windows.

The reminder of the paper is organized as follows: in Section 40.2, we introduce our Extended Dependency Tree-HMM (EDT-HMM) that extends DT-HMM. Section 40.3 describes our indexing scheme. Section 40.4 shows experimental results. Conclusion and future works are given in Section 40.5.

Extended Dependency Tree-Hidden Markov Models

Markov models (Markov Random Fields, Hidden Markov Fields, Hidden Markov Models, Hidden Markov Trees : : :) were extensively and successfully used for texture modeling and segmentation [START_REF] Noda | MRF based texture segmentation using wavelet decomposed images[END_REF]. This is majorly due to their ability to model contextual dependencies and noise absorption [START_REF] Pieczynski | Markov models in image processing[END_REF]. However, their performance depends widely on the model architecture: genuine 2D-models yield better results but exhibits much higher computational complexity [START_REF] Pieczynski | Markov models in image processing[END_REF]. In general, the more complex is the model, the better are the performances.

Nevertheless, for computational complexity reasons, several approaches consider linear models like HMM even if such a model is not suited for two-dimensional data [START_REF] Boudaren | Hidden Markov model based classification of natural objects in aerial pictures[END_REF]. More elaborated approaches resort to 2D-models with simplifying assumption. One simplifying assumption that provides good results with a linear complexity is that assumed in DT-HMM [START_REF] Merialdo | Dependency Tree Hidden Markov Models[END_REF][START_REF] Merialdo | A new approach to probabilistic image modeling with multidimensional hidden Markov models[END_REF]: one site (pixel) may depend on either the horizontal or vertical predecessor, but not on both the same time.

The extension of DT-HMM in this work is motivated by two reasons: the need to compute likelihood probability on non-rectangular shaped windows of different sizes and the need to adopt central pixel (to be labeled) as the dependency tree root since the root shows more interactions with neighbors than other pixels do.

EDT-HMM Overview

Before describing our model principles, let us define the applicability conditions of the EDT-HMM model on a window w with respect to root r.

The window w must fit the following condition:

For each site s of w, s must have at least one neighbor v 2 N s that belongs to w and fulfills: kv; rk < ks; rk where N is the 4-neighborhood and k k is the Euclidean distance.

Let w be a window verifying the condition above, and let r be the center of the window and Y r D fy s =s.i; j / 2 wg be the set of features vectors (RGB for instance) of pixels inside w Y r is then the observable process. Let X be the hidden process. The likelihood probability is given by: Unlike DT-HMM, where each pixel may have a predecessor chosen between two directions, in the EDT-HMM modeling, a pixel s may have a predecessor v chosen randomly from the 4-Neighborhood (up, down, right or left) and verifying the Euclidean distance property. Note that, the neighborhood directions of all pixels of w define a tree structure T like depicted in Fig. 40.1. We note T .s/ D v.

P .Y r =
The likelihood probability to observe Y r given the parameters of the DT-HMM . ; A; B/ can be approximated as follows:

P .Y r = / X T P .Y r =T; / X T X X P .Y r =X; /P .X =T; / X T X X ( Y s2w P .y s =x s ; /P .x s =T; / ) (40.2)
In this paper, we propose to evaluate the likelihood on a set of random dependency trees . The previous equation becomes: where i; j D 1; ::N represent hidden states. Note that a ij only depends on i and j and not on the direction (horizontal or vertical).

P .Y r = / X T 2 P .Y r =T; / X T 2 X X P .Y r =X; /P .X =T; /
To compute the likelihood probability of Eq. (40.3), we define the backward function ˇi .s/ representing the probability of observing the data contained in the sub-tree of T with s as a root starting from the hidden state i .

ˇi .s/ D 8 < : b i .y s / if s is a leaf b i .y s / Q T .v/Ds a ij ˇj .v/ otherwise (40.6)
Note that the likelihood probability of Eq. (40.3) can be evaluated as follows for each dependency tree T :

P .Y r =T; / D N X i D1 i ˇi .r/ (40.7)
This computation exhibits a reasonable complexity (linear with window size).

The extension of the DT-HMM only concerns likelihood probability computation and Viterbi decoding whereas learning is performed the same way as in DT-HMM context.

The Viterbi decoding process can be achieved in a similar way to the likelihood probability computation. On the other hand, learning is performed via an iterative way the same as for the DT-HMM model, since the parameters are the same: Initialize model parameters Choose a random dependency tree T as described above (respecting the Euclidean distance constraint) Perform learning as in a linear framework (like in 1D-HMM)

Classification Scheme

To produce a class map of a given aerial image, we follow the scheme depicted in Fig. 40.2. In the following paragraphs, we describe each step.

Image Unsupervised Segmentation

Before classifying the image pixels, we need to perform an image unsupervised segmentation that fits the following conditions:

Image edges are preserved Pixels of the same region belong necessarily to the same natural object class, i.e. we may have an over-segmentation but not under-segmentation This step serves as a pre-processing one, and will guide the rest of steps of the classification process. One unsupervised segmentation that has been shown to provide good results is the one produced by the EDISON system [START_REF] Comaniciu | Mean shift: A robust approach towards feature space analysis[END_REF][START_REF] Meer | Edge detection with embedded confidence[END_REF] which we use in this work. A sample of a high resolution aerial image (50 cm per pixel) and the corresponding unsupervised segmentation via the EDISON system are provided in Fig. 40.3.

Window Size Computation

Since texture is not a local phenomenon, in order to classify a pixel s, we consider it with its neighborhood. More explicitly, we will compute the likelihood of the data inside a window centered at the pixel under consideration.

Let us denote w s such a window and Y s the data associated to that window. The class s of the central pixel s is the class that maximizes the likelihood probability:

s D arg max 2ƒ P .Y s = / (40.8)
Most approaches adopt a square window of a fixed size for all pixels. A trade-off is usually made so that the window is enough big to correctly classify the central pixel and enough small to preserve the region edges.

In this work, we propose to dynamically compute the window size to allow our system to deal with a maximum amount of information without distorting region edges. The more the pixel to classify is far from the region boundary, the larger is the window whereas edge pixels are classified without considering their neighborhood.

Hence, window size is chosen so that pixels within the window belong to the same region according to an unsupervised over-segmentation of the image. This parameter is obtained from the pre-segmented image. Its value is the maximum value possible so that pixels within the window belong to the same region. A comparative analysis of the window size impact on the accuracy of classification of the aerial image of

Image Pre-classification

To assign a pixel to a class, we compute the likelihood probability of observing the window centered on that pixel according to the EDT-HMM of each natural object class. The pixel is then allocated to the class that maximizes this probability (Fig. 40.5).

The parameters of the EDT-HMM corresponding to each natural object class are obtained after a learning process achieved on mono-class aerial images. To represent each pixel, we used the classical RGB color space. To estimate the parameters of the DT-HMM of each class, we achieve K-Means clustering on pixels of monoclass image of the corresponding class to divide the image pixels on N sub-classes. Subsequently, we obtain the parameters of N Gaussian functions. These parameters serve as an initialization of our EDT-HMM. The final parameters of the model are then obtained after an iterative process as described in the previous section.

Classification Correction

After the previous steps, the resulted class map suffers from the so called salt and pepper phenomenon. This is majorly to the difficulty to distinguish between several similar textures, especially for pixels near boundaries. To overcome this involvedness we propose to merge pixels of the same region (according to the unsupervised segmentation) into the same natural object class with a focus on inner pixels of the region, since those pixels were classified considering larger windows (Fig. 40.6). Explicitly, each region R is assigned the natural class that fits the following rule:

X R D arg max

Experimentation

Data Overview

For our experimentation, we consider real world aerial images with a resolution of 50 cm per pixel (Fig. 40.6). The images were provided by La Régie de Gestion des Données des pays de Savoie, France ( [9]).

The pictures were taken in relatively good light conditions; however, some images suffer from presence of shadow in some parts.

Learning Database

Learning was performed on mono-class images. These images were carefully extracted from the aerial images of the same area of study (Fig. 40.7).

Mono-class Images Generation

To demonstrate the capacity of the DT-HMM to represent natural object textures, we generate mono-class images using the corresponding DT-HMM and compare them to images generated by 1D-HMM and GMM (Fig. 40.8).

Experimental Results

To evaluate the robustness of our aerial images pixels' classification system, we considered three types of test images: Mono-class images, for which the classifier is expected to assign all pixels to the corresponding class (Fig. 40.9) To produce the unsupervised segmentation of areal images, we acknowledge the use of EDISON system software [START_REF] Comaniciu | Mean shift: A robust approach towards feature space analysis[END_REF][START_REF] Meer | Edge detection with embedded confidence[END_REF]. The obtained classification of the mosaic image is very similar to the corresponding ground truth. Notice that the shadow class is not included in the ground truth map since we know to which class the shadowy pixels belong.

Conclusion

In this paper, we proposed an approach that advantageously combines supervised EDT-HMM modeling and unsupervised segmentation to classify land cover pixels. Instead of achieving our classification using a static window size, we resorted to an auto-adaptive window size depending on the position of pixel under consideration towards region boundaries.

Overall, the experimental results show that our system produces satisfactory class maps in a reasonable time given the linear complexity of the modeling. Note that several textures are so similar to each other that it is sometimes very difficult even for a human to distinguish between them.

As future work, we propose to apply EDT-HMM modeling to other kinds of problems.

Fig. 40. 1

 1 Fig. 40.1 Examples of random dependency trees according to DT-HMM (left) and EDT-HMM (right) formalisms

P

  .y s =x s ; /P .x s =T; / ) (40.3) Thereafter, we remind the definition of the Model parameters ; A and B. b i .O/ D P .y s D O=x s D i/ (40.4) P .x s D j =x v D i; / D j if s D r a ij otherwise (40.5)

Fig. 40. 2

 2 Fig. 40.2 Classification scheme

Fig. 40. 3

 3 Fig. 40.3 A sample of high resolution aerial image (RGD 73-74, 2008) (left) and its corresponding unsupervised segmentation (right)

Fig. 40. 4 4 Fig. 40. 5

 445 Fig. 40.4 Window shape and size for different values of Ray s : a-Ray s 2 D 1; b-Ray s 2 D 2; c-Ray s 2 D 4

  Fig. 40.3 is shown in Fig. 40.5.

Fig. 40. 6

 6 Fig. 40.6 Image classification correction Original aerial image (left), class map (right)

Fig. 40. 7

 7 Fig. 40.7 Samples of learning images of classes: tree (left), snow (middle) and water (right)


Idlir Licaj

Mouloud Haddak

Pascal Pochet

Mireille Chiron

Contextual deprivation, daily travel and road traffic injuries among the young in the Rhône Département (France)

This study investigated the effect of the socioeconomic level of the municipality of residence on personal injury road traffic accident risk among young persons of 10-24 years of age in the Rhône Département.

This effect was assessed by comparing incidences of injuries (n = 2,792 casualties) on the basis of three denominators: the resident population of young people, the number of users of each mode and the distances covered by each mode. The results are presented for each type of road users (pedestrians, car passengers, car drivers, motorised two-wheeler riders, cyclists, public transport users).

Young persons from deprived municipalities use motorised-two wheelers, bicycles and the car (as passengers and drivers) less frequently, they walk more and take public transports more often than those from other municipalities.

When considering injury risk, motorised two wheeler injuries among adolescent males, for example, are significantly less frequent in deprived municipalities. But the motorised two-wheeler riders as well as car passengers from deprived municipalities are characterized by an excess injury risk, whether the selected denominator is the number of users or the kilometres travelled by this mode.

For the first time in France, this study has enabled a comparison of the effects of a contextual socioeconomic indicator (the type of municipality of residence, deprived, or not) on daily travel practices and injury incidences among the population, among the users of each mode and per km of travel.

Introduction

Worldwide, road traffic injuries are the principal cause of mortality among young people in the 10-24 year-old age bracket [START_REF] Who | Youth and Road Safety[END_REF]. A number of studies have shown that young people belonging to underprivileged social groups have more road traffic injuries than their more socially advantaged counterparts [START_REF] Cubbin | Socioeconomic status and injury mortality: individual and neighbourhood determinants[END_REF][START_REF] Hasselberg | Socioeconomic differences in road traffic injuries during childhood and youth: a closer look at different kinds of road user[END_REF][START_REF] Hippisley-Cox | Cross sectional survey of socioeconomic variations in severity and mechanism of childhood injuries in Trent 1992-7[END_REF][START_REF] Ferrando | Individual and contextual effects in injury morbidity in Barcelona (Spain)[END_REF][START_REF] Edwards | Serious injuries in children: variation by area deprivation and settlement type[END_REF][START_REF] Fleury | Excess accident risk among residents of deprived areas[END_REF]. The evidence suggests that explanations for these differences should be sought in variations in exposure to risk, rather than in behaviour [START_REF] Laflamme | Social inequality in injury risks: knowledge accumulated and plans for the future[END_REF], though behavioural differences do play some role. Exposure to road risk is defined on the basis of the conditions and forms of travel and the number of trips. Some studies employ measures of risk exposure such as distance travelled, number of intersections crossed or trip duration. [START_REF] Thouez | Measurement of pedestrian exposure to the potential dangers of daily activity-travel patterns in the region of Montreal " Urban Transport[END_REF][START_REF] Spallek | Walking to school: distribution by age, sex and socio-economic status[END_REF]Jungwook et al. 2007a;Jungwook et al. 2007b).

In France, and particularly in the Rhône Département, road traffic injuries peak for males between 15 and 17 years of age and for females between the 22 and 24 years of age (Haddak et al. 2010, forthcoming ).

The minimum legal age for riding a motorized two-wheeler is 14 and for driving a car 18 (except in the case of accompanied driving for 16-17 year-olds).

Although the study of leisure trips is essential in order to gain an understanding of how young people acquire the capacity for independent travel [START_REF] Massot | Auto-mobilité urbaine des adolescents franciliens[END_REF], investigation of the daily mobility of children and adolescents has primarily focused on their home-to-school trips [START_REF] Hjorthol | Do organized leisure activities for children encourage car-use?[END_REF]. Various studies have shown that children's travel practices are strongly dependent not only on their age and gender but also on their parents' standard of living and their residential location [START_REF] Van Vliet | Children's Travel Behavior[END_REF]Paulo 2006;[START_REF] Pochet | Différenciations et inégalités sociales de mobilité chez les jeunes. Analyses de l'enquête ménages déplacements de Lyon 2005-2006[END_REF]. The ownership or mere availability of one or more cars within the household is a key variable for understanding modal choice amongst the young. However, in France, the ease of access to a car is determined not only by residential location, but also by the household's standard of living [START_REF] Claisse | Inégalités de déplacement et équité sociale : revenus, indices et inégalités d'accès à la voiture particulière[END_REF][START_REF] Orfeuil | Transports, pauvretés, exclusions, l'état des savoirs[END_REF][START_REF] Mignot | Vers un droit à la mobilité pour tous, Inégalités, territoires et vie quotidienne[END_REF]. Low income households less frequently own two vehicles than high income households, and more frequently own no vehicle at all.

In addition, they more often purchase secondhand cars and the higher average age of their vehicles [START_REF] Nicolas | Towards sustainable mobility indicators: application to the Lyons conurbation[END_REF]) may affect their availability and influence crash risk and severity, particularly in the case of young male drivers, as has been observed in the United States [START_REF] Males | Poverty as a determinant of young drivers' fatal crash risks[END_REF]. The expensive cost of procuring a driving licence may affect the daily travel of low income young and therefore have consequences on social exclusion, on the example of non-western immigrants in Norway [START_REF] Priya | Dynamics of transport and social exclusion: Effects of expensive driver's license[END_REF].

The combined use of data on daily travel and road traffic injuries is still unusual in epidemiological studies. The main reason for this is that the necessary data is difficult to collect. Several measures are used to define road risk exposure, for example, the distance travelled [START_REF] Pucher | Promoting safe walking and cycling to improve public health: lessons from The Netherlands and Germany[END_REF][START_REF] Harrison | Exposure survey of motorcyclists in New South Wales[END_REF], the number of trips [START_REF] Pucher | Promoting safe walking and cycling to improve public health: lessons from The Netherlands and Germany[END_REF][START_REF] Rice | Nighttime driving, passenger transport, and injury crash rates of young drivers[END_REF], the number of streets crossed in the case of pedestrians [START_REF] Posner | Exposure to traffic among urban children injured as pedestrians[END_REF] and journey times [START_REF] Lee | Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida[END_REF]. A Canadian study from some years ago (Macpherson et al. 1998), after showing that underprivileged children between the ages of 5 and 12 crossed more roads on foot, demonstrated that there is a strong positive correlation between the number of roads crossed and the number of pedestrian injuries. However, most of these studies considered a single transport mode and did not include socioeconomic data.

This paper compares road trauma data (from the Rhone road trauma registry) with travel data in order to gain a better understanding of the origin of the social and geographical inequalities with regard to road traffic injuries which are observed in France and other industrialized countries.

The goals are as follows: first, to investigate the effect of the socioeconomic level of the municipality of residence on the use of different transport modes by young persons of 10-24 years of age in the Rhône personal injury road traffic accident risk as assessed on the basis of three denominators: total population, the number of users of each mode and the distances covered by each mode.

Material and methods

The Rhône Département and the Deprived Areas

The Rhône is a French Département (1,600,000 inhabitants) in the Rhône-Alpes Region whose main urban centre is the Lyon conurbation (1,200,000 inhabitants).

France has so-called Zones Urbaines Sensibles (ZUS) which are deprived areas which have been identified by the public authorities as priority targets for urban policy. These areas are characterized by large apartment buildings and relatively poor housing conditions, a pronounced imbalance between the number of dwellings and the number of jobs and low incomes. In addition, ZUS residents are more often in casual employment than those living in other areas.

The Rhône contains 293 municipalities and has a population of 1,669,600 of which 191,682 (11.5%) live in 30 ZUS's which are spread over 25 municipalities (INSEE 2006). Some municipalities have two or even three ZUS's. Also at the time of the last census (1999), the unemployment rate stood at 22.4% in the Rhône's ZUS's, compared with 11.4% in the département as a whole. In addition, the under 25 year-old age group, individuals with no qualifications and single parent families were over-represented in the ZUS's: in the ZUS's the under-25s accounted for 39.2% of the population, unqualified persons (without any formal academic qualifications) accounted for 33.3% of the population and single parent families accounted for 21.5% of the population, while the percentages for the rest of the Rhône were respectively 33.0%, 18.7%, and 13.0%.

The above characteristics show that living in a ZUS would seem to be a good indicator of poor social position. However, our data only report the residential municipality of casualties. We therefore decided to use living in a municipality with one or more ZUS's (deprived municipalities, in opposition with other municipalities: without ZUS) as an indicator. In such municipalities, on average 35% of persons of under 25 years of age live in a ZUS. In order to obtain an accurate evaluation of this socio-geographical indicator we shall compare the median taxable income per consumption unit in the two types of municipality (INSEE France 1999). The median income in the municipalities with a ZUS was €15,719 per year with an interquartile interval of [12,120] whereas in the municipalities without a ZUS, the median annual income was €19,789 with an interquartile interval that is disjoint from the previous one [17,829]. The use of this sociogeographical indicator thus allows us to consider urban contexts which are contrasting with regard to the incomes of their residents.

The Rhône Registry

The Registry contains all the individuals who sustain injuries in road traffic accidents in the Rhone Département. A total of 282 public and private health care departments to which road traffic accident casualties may be taken contribute data to the Registry. It was developed by the UMRESTTE, in collaboration with the Département's Fire and Emergency Service and the Rhône Département Road Trauma Registry Association (ARVAC) [START_REF] Laumon | A French road accident trauma registry: first results[END_REF]. Certain details about the accident (location, date, time of day, vehicles involved) and some personal data about the casualty are recorded.

The inclusion criteria are the location of the accident (Rhône), an accident involving a moving vehicle (including roller-skates and skateboards), and the presence of at least one AIS injury (AIS 1990 Revision). Pedestrians who fall on their own are therefore not included.

The municipality in which the accident occurs was classified according to whether or not it contained a ZUS. In the case of the city of Lyon, each district was considered to be a municipality. Casualties injured in the Rhône but living outside the Rhône (14%) were excluded.

The 2005-2006 Household Travel Survey

The Lyon Household Travel Survey (HTS) we used was conducted between November 2005 and May 2006. This survey is representative of the population of households living within the study area, which includes the entire Rhône Département. In the 2,968 surveyed households in the Rhône, the 5,102 persons aged over 4 years who were interviewed individually provided information about their personal characteristics and mobility on one day. Mobility was identified on the basis of all the trips made the day before the interview. In addition, the survey collected details about respondents' customary mode use, asking them to choose between six alternatives with regard to weekday travel: The question about respondents' travel behaviour, both customary and on the day before the survey related to weekdays outside school holidays.

We constructed a binary variable for each transport mode. Mode use varied greatly in intensity between the different modes. That is the reason why, in the case of rare modes or modes with a high per kilometre injury risk such as M2Ws and bicycles, the binary risk exposure variable contrasted those who used the mode, even exceptionally, with those who did not (items 1, 2, 3, 4 versus 5). The same contrast was chosen in the case of car drivers. In the case of car passengers and public transport users, who were more frequently among the young, the contrast was between very frequent users and non-users, which in this case also included occasional users (items 1 vs 2 to 5).

The frequency of usual walking was not described in the HTS on the grounds that walking is a generalized practice either as a distinct travel mode or as in addition to another mode. We have used the characteristics of individuals' travel the day before the interviewer visited the household (walking trips conducted on a standard day, during the week and outside school holidays), making a dichotomic distinction between individuals who used walking as a distinct mode of transport and those who did not.

In the case of the trips made the day before the interview, the distances covered by each mode are also estimated. We consider that these are representative of the distances covered by all users on weekdays.

The results that are presented involve weighted data, representing a projection of the Rhône Département HTS sample to this entire area. The weight assigned to each household in the survey depends on the sampling rate in its zone, corrected by a factor based on the average size of the households in the zone.

The survey contained 46% of young people living in a municipality with a ZUS while, based on the 2006 INSEE census, the percentage of young people living in such a municipality was 54%.

Analyses

The analyses covered young people of 10-24 years of age living in the Rhône Département on the basis of three age categories: 10-13, 14-17 and 18-24 years.

Initially, the use of the different transport modes was described on the basis of age, gender, and municipality type (with a ZUS, i.e. deprived, and other municipalities without a ZUS): rate of use of the various modes and kilometres travelled on weekdays outside school holidays. When the mean distances travelled were not significantly different between deprived and other municipalities we have presented a single mean distance with its 95% confidence interval for each mode and for the three age classes. When the distances covered were different in the two types of municipalities, both values have been given. The comparisons have been made with surveymeans procedure in SAS software [START_REF] Cassell | Wait Wait, Don't Tell Me… You're Using the Wrong Proc![END_REF] The incidence ratios (incidence rate in deprived municipalities/incidence rate in other) for injury accidents according to age and gender and each mode were then calculated, taking in turn as denominators for incidence rates the total populations, the number of users for each transport mode as defined in Section 2.3 and the number of kilometres travelled by the users in question. The number of customary users was calculated by applying the rate of use obtained in the HTS to the population of the two categories of municipalities in the département.

In order for these incidences to be meaningful, we will retain only those injuries that occurred outside weekends and outside school holidays in 2005-2006 in our calculation instances. All the statistical analyses were performed using the SAS software package (version 9.2).

Results

The effect of the type of municipality on the use of transport modes

The use rates for the different modes

A total of 3,885 young persons aged between 10 and 24 years were interviewed in the Rhône Département. After weighting, the distribution of the population (305,168) according to age and gender was as follows: 27% between 10 and 13 years; 30% between 14 and 17 years and 51% were males. Table 1 shows the percentages of individuals for each category of age, gender and type of municipality. For example, 15% of the young males in the deprived municipalities travelled everyday or almost everyday as car passengers, while 45% had made a walking trip the day before the interview and only 6% used a M2W both on a regular or an exceptional basis.

Car use (as a passenger and driver) was fairly similar among young males and young females, however it was very much linked to the socioeconomic level of the municipality of residence. For all age and gender categories, car travel was approximately half as common among young people from deprived municipalities as among their counterparts from other municipalities.

Likewise, driving a car (regularly or on an exceptional basis) was less common in deprived municipalities, and this was particularly true for young males aged between 16 and 17 years (3 times less)

and young females aged between 18 and 24 years (1.4 times less).

Whatever the type of municipality, 3 times more young males used M2Ws. Overall, and for each of the two relevant age categories, three times fewer young persons from deprived municipalities reported M2W use than those living in other municipalities.

For both types of municipalities, bicycle use was more frequent among young males. Bicycle use (both regular and exceptional) was lower in deprived municipalities for both genders, particularly among 10-13 year-olds, while there was almost no difference between deprived and other municipalities among 18-24 year-olds.

In the case of walking, no marked gender effect is apparent. However, the use of walking was greater in deprived municipalities, particularly among 10-17 year-olds.

Public transport use is a specific case, it was the young females from deprived municipalities who used public transport most, followed by young males from deprived municipalities while those who used it least were the young males from other municipalities.

Distance travelled by the young persons, for each mode

Among children of 10-13 years of age, the daily distances travelled by users of each mode on the day before the survey were, on average, 2.1 km [1.9-2.3] by foot, 5.3 km [4.5-6.1] by bicycle, 10.0 km [8.9-11.0] by car as a passenger and 9.6 km [8.5-10.6] on public transport.

For the 14-17 year olds, the daily distances were higher: on average, this group covered 2.6 km [2.4-2.8] on foot, 6.3 km [3.5-9.1] by bicycle, 11.9 km [7.6-16.3] by M2W and 14.0 km [12.4-15.7] The young persons aged between 18 and 24 living in a deprived municipality also covered longer distances on foot than those from other municipalities. The young males from deprived municipalities also covered longer distances by bicycle. However, both the young males and young females from deprived municipalities travelled shorter distances than those from other municipalities by car and M2W.

These differences were not significant.

Young females from deprived municipalities also travelled shorter distances on public transport (14.9 km [13.2-16.6] vs 22.1 km [19.2-25.0]) than the young females from other municipalities. Similarly, the young males from deprived municipalities travelled shorter distances by public transport (deprived vs other: 16.7 km [14.9-18.6] vs 23.5 km [19.6-27.4]).

Ultimately, the distances travelled by each mode by the young males and females were fairly similar.

The influence of the type of municipality on injury frequency

In the Rhône as a whole, 6,944 young people of between 10-24 years of age were injured or killed in a road traffic accident in 2005 and 2006, and 5,945 of these lived in the Département. This study concentrates on the accidents involving young people that occurred outside weekends and school holidays. These involved 2,792 casualties (47%) and 321 days (44%). We removed 247 casualties (i.e.

8.8%) whose municipality of residence was unknown. Ultimately, the study included 2,545 casualties, of whom 67.6% were male. The distribution between the three age groups was as follows: 11% were aged between 10 and 13 years, 25% between 14 and 17 years and 64% between 18 and 24 years.

In the case of the young males (Table 2), the incidence ratios (deprived municipalities/other) among the population reveal an excess risk of injuries on a bicycle for individuals over 14 years of age and on foot for individuals under 18 years of age in deprived municipalities. However, M2W

injuries among adolescent males were less frequent in deprived municipalities. For young females, there was an excess injury risk in deprived municipalities on foot at all ages, and as a car passenger over 18 years of age. Fewer females were injured in two-wheeler accidents, and the difference between deprived and other municipalities for this mode is not significant.

The incidence ratios among the users of each mode reveal significant injury excess risks in deprived municipalities for male car passengers of under 18 years of age. (It was not the case for 18-24 year-old drivers). Taking into account the number of users of each mode gave another vision of M2W risk in deprived municipalities: there was an excess injury risk among M2W riders from deprived municipalities. The opposite applied when incidences were related to the whole of young population.

In the case of young females (Table 2) as car passengers, the excess risk in deprived municipalities among those of 18 years of age and over increased, while the excess risk for pedestrians disappeared.

The incidence ratios per km travelled for each mode reveal an excess risk for car drivers of both genders in the 18-24 year-old age group from deprived municipalities. The excess risk for M2W users in deprived municipalities was even greater than with the previous indicator among young males of over 18 years of age, increasing from 1.68 to 6.83. However, they had fewer bicycle accidents than young males of the same age from other municipalities. In the case of females of over 18 years of age, there was also an excess risk for drivers from deprived municipalities, and this excess risk was even greater for passengers.

Thus, considering the number of users of each mode reveals that in the case of young males, it is more dangerous to be a car passenger or a M2W user when one lives in a deprived municipality.

If the distances travelled by each mode are considered, the risk is even greater for young male motorists, M2W users and child cyclists of between 10 and 17 years of age from deprived municipalities. Among females, taking this factor into account increases the risk for motorists of between 18 to 24 years of age from deprived municipalities.

Discussion

For the first time in France, this study has enabled a comparison of the effects of a contextual socioeconomic indicator (the type of municipality of residence, deprived, or not) on daily travel practices and injury incidences among the population, among the users of each mode and per km of travel.

Customary use of certain modes on weekdays and outside school holidays involves, essentially, but not exclusively, home-to-school trips or occupational trips. This information was supplemented by the kilometres of travel for each mode on the day before the study. Mode use differed considerably between the two types of municipality. The young persons from deprived municipalities used M2Ws and the car (as passengers and drivers) less frequently than those from other municipalities. This result concurs with the findings of an American study by [START_REF] Van Vliet | Children's Travel Behavior[END_REF] which found that children and adolescents from high-income families were more often transported by car. Another study which concentrated exclusively on the home-to-school trips of American students has shown that there are no sociodemographic differences between walking and cycling as opposed to using the car [START_REF] Mcmillan | Johnny Walks to school-Does Jane? Sex differences in children's active travel to school[END_REF].

Use of a particular mode of transport for one's daily trips obviously depends on a large number of factors in addition to the socioeconomic level of one's residential municipality and the ease or difficulty of accessing a car. The status of a young person in relation to the activity (school, work, unemployment), and, especially for those in education, the type of educational establishment attended (secondary school or further education establishment…), and the population density and centrality of the residential location [START_REF] Pochet | Différenciations et inégalités sociales de mobilité chez les jeunes. Analyses de l'enquête ménages déplacements de Lyon 2005-2006[END_REF]. The use of transport modes is also influenced by the attributes of urban form [START_REF] Mcmillan | The relative influence of urban form on a child's travel mode to school[END_REF] as well as physical characteristics of the environment or the experience the young person's parents have had of hazards on the road [START_REF] Fyhri | Children's independent mobility to school, friends and leisure activities[END_REF].

This study is an original contribution to the literature as it considers six different transport modes separately when investigating young persons' accidents, taking account of the number of users of each mode and the distances travelled for each.

In the present study, the M2W users from deprived municipalities are characterized by an excess injury risk, whether the selected denominator is the number of users or the kilometres of travel, which was masked by a lower rate of M2W use, leading to lower risk when using the population rather than riders as the denominator. This result shows the importance of comparing accident data with data on daily travel in order to qualify, clarify or better explain some apparent findings.

Likewise, in the case of car passengers, taking account of exposure (proportion of users and the distances covered) reveals a significant excess risk per km of travel among young males and greater excess risk among young females, although car use is lower in deprived municipalities. In the case of car drivers, there is a paradoxical finding, namely a reduced risk in relation to the number of users and an excess risk in relation to the distances travelled, which is partly explained by the lower distances covered in deprived municipalities.

To explain the excess risk per km travelled in deprived municipalities, we can make the hypothesis that risky behaviours are more frequent in deprived areas [START_REF] Vaez | Impaired driving and motor vehicle crashes among Swedish youth: an investigation into drivers' sociodemographic characteristics[END_REF].

The behaviours in question can include a failure to wear a helmet or a safety belt, for example.

The age of the vehicles may also play a role. Moreover Janke claims that accidents per mile exaggerate the measure of risk in particular for low-mileage groups such as teenagers: people driving low mileages tend to accumulate much of their mileage on congested city streets with twoway traffic [START_REF] Janke | Accidents, mileage, and the exaggeration of risk[END_REF].

The contextual study does not allow us to measure the proportion of risk that can be attributed to individual socio-economic characteristics, as these are linked to our variable of interest (municipality of residence). These individual variables were not available. Considering modal use at the contextual level nevertheless considerably improves our understanding of the raw differences in traffic injury incidences that exist according to social context. To our knowledge no existing study in this area has applied this method.

As we have no data on the mobility of the young person's at weekends and during school holidays, in the context of this study we have not been able to examine holiday periods, and have targeted those killed or injured in accidents that occurred between Monday and Friday and outside school holidays. However, in France, social inequalities have more effect on long distance travel (summer holidays) than local travel, on weekend travel than week-day travel, and on leisure travel than home-to-school travel. The degree of choice and the constraints associated with the young peoples' place of study, which vary according to social class, influence amount of travel and the transport modes used during the week (Paulo 2006). The difference between deprived and other municipalities would perhaps be greater if we had been able to study all the days in the year. It would therefore be interesting to conduct a study of this type for weekends and school holidays.

The Rhône Registry only contains accidents that occurred in the Département. It is possible that the residents of the more advantaged municipalities, a lower proportion of whom reside within the Lyon conurbation and who travel greater distances are more often injured outside the Département, which could lead to an overestimation of the risk ratio between deprived municipalities and the others. However, we know that most accidents occur near people's homes.

In the Registry, in 2005Registry, in -2006, 85, 85.6% of the 10-24 year-old casualties of accidents which occurred in the Rhône Département were residents of the Rhône Département too.

By considering only the nature of the municipality (with or without a ZUS), we have, because of the limitation of the data, "diluted" our analysis by classifying individuals who live in a municipality with a ZUS but not in the ZUS itself as exposed. Nevertheless, our indicator "municipality with a ZUS" seems to be valid, as the presence of a ZUS is strongly linked to the socioeconomic level of the municipality, as we have shown. This indicator is available for the whole of France.

The coexistence of the Registry and the household travel survey has made it possible to compare risk exposure data with data on the road traffic injuries that occurred in the same period and in the same area, which has revealed that journeys made by car and two wheelers are more dangerous for young persons who live in deprived municipalities.

1.

  Every day or almost, 2. At least two trips per week, 3. At least two trips per month, 4. On an exceptional basis, 5. Never Details on customary usage were collected for the following modes: -Motorized two wheelers (denoted by M2W), -Car as a passenger,

Table 1 Use of modes (%) according to municipality of residence (deprived, other), gender and age

 1

			Deprived municipalities					Other municipalities
		10-13 years	14-17 years	18-24 years	All	10-13 years	14-17 years	18-24 years	All
								Males
	Population	18982	20010	53082		92075	22150	22986		37213	82 348
	Car pass.	6357	29%	3654	16%	2821	7%	15%	11992 55%	7774	31%	3446	14%	32%
	Car driver *	-	-	514	5%	23839 62%	50%	-	-	1647	15% 16488 66%	50%
	M2W	-	-	1201	5%	2681	7%	6%	-	-	4583	18%	3185	13%	15%
	Bicycle	11167 51% 12702 57% 18818 49%	52%	15328 70%	14792	69% 11843 47%	58%
	Pedestrian	14274 65% 11361 51% 11592 30%	45%	10042 46%	8861	35%	7674	31%	37%
	Public transport	4198	19% 11667 52% 18897 49%	42%	3499	16%	9930	39%	9412	38%	32%
							Females
	Population	18468	19686	60838		98992	21132	21572		37565	80269
	Car pass.	4385	25%	4044	21%	3968	9%	15%	10368 53%	7617	35%	3841	14%	32%
	Car driver *	-	-	814	8%	22713 50%	43%	-	-	1901	15% 19015 70%	52%
	M2W	-	-	548	3%	920	2%	2%	-	-	1469	7%	1353	5%	6%
	Bicycle	7175	40%	6830	36% 16944 38%	38%	9394	48%	8695	40% 10479 39%	42%
	Pedestrian	10890 61%	9083	47% 18627 41%	47%	9477	49%	6063	28%	7029	26%	33%
	Public transport	5436	31% 10602 55% 25969 58%	51%	3234	17%	9059	42% 12483 46%	36%

1-Car passenger, public transportation : daily or almost daily use on weekdays 2-Car driver M2W and bicycle users : regular or exceptional use on weekdays 3-Walking the day before the survey, on weekdays outside school holidays

 [0.05-13.4]


Xiaoning Guo
email: xyguo@sxicc.ac.cn

Guojuan Zhi

Xiaoyan Yan

Guoqiang Jin

Xiangyun Guo

Pascal Brault
email: pascal.brault@univ-orleans.fr

Methane Combustion over Pd/ZrO 2 /SiC, Pd/CeO 2 /SiC, Pd/Zr 0.5 Ce 0.5 O 2 /SiC Catalysts

Keywords: High surface area SiC, Pd/SiC catalysts, Zr 0.5 Ce 0.5 O 2 solid solution, methane combustion

The performances of different promoters (CeO 2 , ZrO 2 and Ce 0.5 Zr 0.5 O 2 solid solution) modified Pd/SiC catalysts for methane combustion are studied. XRD and XPS results showed that Zr 4+ could be incorporated into the CeO 2 lattice to form Zr 0.5 Ce 0.5 O 2 solid solution. The catalytic activities of Pd/CeO 2 /SiC and Pd/ZrO 2 /SiC are lower than that of Pd/Zr 0.5 Ce 0.5 O 2 /SiC. The Pd/Zr 0.5 Ce 0.5 O 2 /SiC catalyst can ignite the reaction at 240 o C and obtain a methane conversion of 100% at 340 o C, and keep 100 % methane conversion after 10 reaction cycles. These results indicate that active metallic nanoparticles are well stabilized on the SiC surface while the promoters serve as oxygen reservoir and retain good redox properties.

Introduction

Supported palladium catalysts have been found to have excellent activity toward the catalytic combustion of methane, which has been found to be more environmentally friendly than traditional flame combustion due to lower emissions of NO x , CO, and unburned hydrocarbons [1,2] .

Usually, the supports of Pd-based catalysts are thermal insulators, such as SiO 2 and Al 2 O 3 . The methane combustion is a strongly exothermic reaction, therefore the reaction heat accumulated on isolated metal nanoparticles makes them easily sintered [3][4][5] . It has been established that SiC can be a potentially excellent catalyst support for various reactions since SiC has admirable chemical stability and high thermal conductivity and stability [6][7][8] . Our previous study showed that Pd/SiC catalyst using etched SiC nanowires as the support can completely convert CH 4 at about 390°C and run 10 reaction cycles without any decrease in the catalytic activity [9] .

It is well known that CeO 2 and ZrO 2 are effective promoters for noble-based combustion catalysts because they can enhance the dispersion and stability of metallic active phases. However CeO 2 is easily sintered at high reaction temperatures, and ZrO 2 has the drawbacks of high cost and relatively low surface area [10][11][12][13] . Therefore, researchers have suggested that formation of a Ce 0.5 Zr 0.5 O 2 solid solution by adding ZrO 2 to CeO 2 can improve oxygen storage capacity, redox properties, thermal resistance and catalytic activity of catalysts at lower temperatures [14][15][16][17] . In this work, we investigate Pd/SiC catalysts modified by CeO 2 、ZrO 2 and Zr 0.5 Ce 0.5 O 2 solid solution respectively, which show excellent activity and stability for methane combustion.

Experiments

Catalyst Preparation

The catalysts are 1wt.% Pd supported on either SiC (S BET =50.8m 2 /g) [18,19] or SiC modified with 1% promoters (ZrO 2 , CeO 2 and Zr 0.5 Ce 0.5 O 2 ) and were prepared by the impregnation method. First, 0.4g SiC was added into 20ml aqueous solution of Zr(NO 3 ) 4 •5H 2 O (0.05wt.%), Ce(NO 3 )

Catalytic test

The catalytic performance of the different catalysts for the methane combustion was carried out in a fixed-bed quartz reactor with an inner diameter of 8 mm at atmospheric pressure, and the mixture of O 2 (20 %)/CH 4 (1 %)/N 2 (79 %) was used as the feedstock. 300 mg of the catalyst was packed between two layers of quartz wool. The hourly space velocity was controlled to be 10000h -1 . Since the deactivation of the SiC-supported catalysts usually demands a long time, a repeated heating-then-cooling cycle method was employed to estimate the stabilities of the catalysts as previously reported [9] .

Catalyst characterization

The crystalline phases of different catalysts were characterized by a Rigaku D-Max/RB X-ray diffractometer (XRD) with Cu Kα radiation. The microstructures of the catalysts were analyzed by using a JEOL-2010 transmission electron microscope (TEM) and JEM-2010 high-resolution transmission electron microscope (HRTEM). X-ray photoelectron spectroscopy (XPS) were carried out on a Kratos XSAM800 spectrometer by using Al Ka (hk = 1486.6 eV) X-ray source.

Results and discussion

XRD and XPS characterization of fresh catalysts

From the XRD patterns of different fresh catalysts shown in Fig. 1, the strong diffraction peak (2θ=35.8°) can be indexed to β-SiC. All the catalysts present an obvious peak at 2θ=33.8°, which attributed to the tetragonal PdO. Pd/ZrO 2 /SiC presents two peaks at near 28.1° and 31.4° (2θ), assigned to monoclinic ZrO 2 . The main peaks at 28.8° and 33.3° (2θ) in the XRD patterns of Pd/CeO 2 /SiC correspond to the cubic, fluorite structures of CeO 2 . The diffraction peaks ascribed to ZrO 2 were not observed in Pd/Zr 0.5 Ce 0.5 O 2 /SiC. However the 2θ-values of CeO 2 peaks increase from 28.8° and 33.3° to 29.1° and 33.5°, respectively. This is due to Zr 4+ has incorporated into the CeO 2 lattice, and caused the change of CeO 2 crystal phase and the formation of Zr 0.5 Ce 0.5 O 2 solid solution [20,21] . Fig. 2 represents the XPS spectra of fresh Pd/ZrO 2 /SiC, Pd/CeO 2 /SiC and Pd/Zr 0.5 Ce 0.5 O 2 /SiC. The peak at 337.6 -337.7 eV shown in Fig. 2a corresponds to the Pd 3d 5/2 . Generally, there are three components with Pd 3d 5/2 bonding energy (BE) values in 334.7-335.1 eV, 337.6-337.8 eV and 338.4-338.5 eV, which are usually attributed to Pd 0 , Pd 2+ and Pd 4+ , respectively [START_REF] Ikeo | Handbook of X-Ray Photoelectron Spectroscopy[END_REF][START_REF] Simplício | [END_REF][24] . Therefore, the valence of Pd atoms in these three catalysts is +2. The peaks of Pd/ZrO 2 /SiC at about 181.4 and 183.7 eV shown in Fig. 2b correspond to the Zr 3d 5/2 and Zr 3d 3/2 , respectively.

The difference in the binding energies between the Zr 3d 5/2 and Zr 3d 3/2 photoemission feature is 2.3 eV, which is in agreement with the reported value [25] . Nelson and Schulz reported that the band located at 900.2-900.6 eV was the Ce 3d 3/2 ionization and the band located at 881.7-882.0 eV was the Ce 3d 5/2 ionization [25] . Therefore, the peaks of Pd/CeO 2 /SiC at about 900.2 and 881.7eV shown in Fig. 2c should be the principal binding energies of Ce 3d 3/2 and Ce 3d 5/2 , respectively. The peaks at about 897.6 and 916.1 eV are satellites arising from the Ce 3d 3/2 and Ce 3d 5/2 . According to the literature, in the Ce-Zr mixed oxide with 15-80% of Ce the peak position of Zr 3d 5/2 usually occurs at 181.7-181.8 eV and the Ce 3d 5/2 peak at 881.8-882.1 eV [26] . In the case of our study, the peak of Pd/Zr 0.5 Ce 0.5 O 2 /SiC at about 181.7 eV (Fig. 2b) corresponds to the Zr 3d 5/2 , while the peak of Pd/Zr 0.5 Ce 0.5 O 2 /SiC at about 882.1 eV (Fig. 2c) corresponds to the Ce 3d 5/2 . Thus, the majority of ZrO 2 and CeO 2 of Pd/Zr 0.5 Ce 0.5 O 2 /SiC are in the form of Ce 0.5 Zr 0.5 O 2 solid solution but not in the form of separate ZrO 2 and CeO 2 . From Fig. 2c, the peak of Pd/Zr 0.5 Ce 0.5 O 2 /SiC at about 900.8 eV is the principal binding energies of Ce 3d 3/2 , and the peaks at about 897.8 eV and 916.3 eV are satellites arising from the Ce 3d 3/2 and Ce 3d 5/2 .

TEM and HRTEM characterization

Fig. 3 shows TEM images of fresh and used Pd/SiC catalysts. From Fig. 3a, the lattice spacings of nanoparticle are 0.262 nm and 0.269 nm, which are indexed as the (101) and (002) planes of tetragonal PdO. The PdO nanoparticles in Pd/SiC have a narrow size distribution from 2 to 4 nm and an average diameter of 2.6 nm according to our statistical analysis (Fig. 3a). From Fig. 3b, the lattice spacing of nanoparticles is 0.263 nm, which corresponds to (101) planes of tetragonal PdO, claiming the active phase PdO has not been decomposed into metallic Pd during the cyclic reaction. The average size of PdO particles only has a slight increase from 2.6 to 2.8 nm after the cyclic reaction (Fig. 3b). The above results suggest that PdO nanoparticles are very stable on the surface of SiC because of the high thermal conductivity and chemical stability of the high surface area SiC, which can effectively hinder the sintering of active nanoparticles and thus stabilize them. Fig. 4 shows TEM images of fresh Pd/SiC catalysts modified by ZrO 2 , CeO 2 and Zr 0.5 Ce 0.5 O 2 .

PdO nanoparticles could not be found in Fig. 4 possibly because they are too small to be found out.

The lattice spacing of nanoparticles is 0.205 nm, which is attributed to the interplanar spacing of (211) plane of monoclinic ZrO 2 (Fig. 4a). The lattice spacings of 0.311, 0.312, 0.315 and 0.276 nm are indexed as the ( 111) and (200) of cubic CeO 2 (Fig. 4b). The lattice spacing of 0.291 nm corresponds to the (111) planes of cubic Zr 0.5 Ce 0.5 O 2 . Compared with pure CeO 2, the interplanar spacing has a little decrease, suggesting that Zr 4+ has inserted into the CeO 2 crystal lattice, which may cause the change of CeO 2 crystal phase [21] . between the active phases and supports, therefore improve the catalytic activity and stability [12,13] .

Catalytic performances

Nevertheless, Zr 0.5 Ce 0.5 O 2 solid solution modification can significantly reduce T 10% from 285 o C to 240 o C and T 100% from 370 o C to 340 o C respectively, suggesting that Zr 0.5 Ce 0.5 O 2 solid solution could strongly improve oxygen storage capacity and thermal resistance [14][15][16][17] .This also means that the combustion reaction could be finished in a narrow temperature range (around 100 o C). Fig. 5b shows the results of stability of Pd/Al 2 O 3 . The final methane conversion left only 67.4% at 370 o C after 10 reaction cycles mainly because of the sintering of γ-alumina and coalescence of PdO nanoparticles [4,5,27] . However, the Pd/SiC (Fig. 5c) and Pd/Zr 0.5 Ce 0.5 O 2 /SiC (Fig. 5d) catalysts can keep the methane conversion at almost 100 % after 10 reaction cycles, indicating that high surface area SiC supported catalysts have excellent stability in methane combustion. This further confirms the previous results from TEM images of catalysts that high surface area SiC can significantly hinder the migration and coalescence of active phase nanoparticles and thus stabilize them.

Conclusion

Pd-based catalysts using high surface area SiC (50.8m 2 /g) modified by CeO 2 , ZrO 2 and Ce 0.5 Zr 0.5 O 2 solid solution as the support could effectively increase the dispersion and activity of active metallic phases. XRD and XPS studies showed that Zr 4+ could be incorporated into the

Fig. 5

 5 Fig.5shows the results of activities and stabilities of different catalysts. From Fig.5a, T 10%

CeO 2

 2 lattice to form Zr 0.5 Ce 0.5 O 2 solid solution. T 10% and T 100% of Pd/Zr 0.5 Ce 0.5 O 2 /SiC are 240 o C and 340 o C respectively and the reaction could be finished in a narrow temperature range (around 100 o C) certainly due to the excellent oxygen storage capacity, redox properties, thermal stability and low temperature catalytic activity of Ce 0.5 Zr 0.5 O 2 solid solution. However, Pd/CeO 2 /SiC and Pd/ZrO 2 /SiC have lower catalytic activity than Pd/Zr 0.5 Ce 0.5 O 2 /SiC. All the SiC-based catalysts can keep the methane conversion of almost 100 % after 10 reaction cycles suggesting that active nanoparticles are well stabilized on the SiC surface while the promoters serve as oxygen reservoir and retain good redox properties.

Fig. 1

 1 Figures captions:

Fig. 2

 2 Fig. 2 XPS patterns of Pd/ZrO 2 /SiC, Pd/CeO 2 /SiC and Pd/Zr 0.5 Ce 0.5 O 2 /SiC: (a) Pd 3d, (b) Zr 3d and (c) Ce 3d.

Fig. 3

 3 Fig. 3 TEM images of fresh (a) and used (b) Pd/SiC, and the inserted figures are the HRTEM of different catalysts themselves.

Fig. 4

 4 Fig. 4 TEM images of different fresh modified catalysts: Pd/ZrO 2 /SiC (a), Pd/CeO 2 /SiC (b) and Pd/Zr 0.5 Ce 0.5 O 2 /SiC (c); and the inserted figures are HRTEM of different catalysts themselves.

Fig. 5

 5 Fig. 5 Catalytic performances of different catalysts for methane combustion: the activities of different catalysts (a); and cyclic reaction results of Pd/Al 2 O 3 (b), Pd/SiC (c) and Pd/Zr 0.5 Ce 0.5 O 2 /SiC (d).

Fig. 1

 1 Fig. 1 XRD patterns of different catalysts.

Fig. 2

 2 Fig. 2 XPS patterns of Pd/ZrO 2 /SiC, Pd/CeO 2 /SiC and Pd/Zr 0.5 Ce 0.5 O 2 /SiC: (a) Pd 3d, (b) Zr 3d and (c) Ce 3d.

Fig. 3

 3 Fig. 3 TEM images of fresh (a) and used (b) Pd/SiC, and the inserted figures are the HRTEM of different catalysts themselves.

Fig. 4

 4 Fig. 4 TEM images of different fresh modified catalysts: Pd/ZrO 2 /SiC (a), Pd/CeO 2 /SiC (b) and Pd/Zr 0.5 Ce 0.5 O 2 /SiC (c); and the inserted figures are HRTEM of different catalysts themselves.

Fig. 5

 5 Fig. 5 Catalytic performances of different catalysts for methane combustion: the activities of different catalysts (a); and cyclic reaction results of Pd/Al 2 O 3 (b), Pd/SiC (c) and Pd/Zr 0.5 Ce 0.5 O 2 /SiC (d).

  3

Acknowledgment

The work was financially supported by Shanxi Province (Ref: 2008011014-1), NSFC (20973190) and MOST (Ref: SKLCC-2008BWZ010 and 2011CB201405).