Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> We develop anomaly detectors, i.e., detectors that do not presuppose a signature model of one or more dimensions, for three clutter models: the local normal model, the global normal mixture model, and the global linear mixture model. The local normal model treats the neighborhood of a pixel as having a normal probability distribution. The normal mixture model considers the observation from each pixel as arising from one of several possible classes such that each class has a normal probability distribution. The linear mixture model considers each observation to be a linear combination of fixed spectra, known as endmembers, that are, or may be, associated with materials in the scene, and the coefficients, interpreted as fractional abundance, are constrained to be nonnegative and sum to one. We show how the generalized likelihood ratio test (GLRT) may be used to derive anomaly detectors for the local normal and global normal mixture models. The anomaly detector applied with the linear mixture approach proceeds by identifying target like endmembers based on properties of the histogram of the abundance estimates and employing a matched filter in the space of abundance estimates. To overcome the limitations of the individual models, we develop a joint decision logic, based on a maximum entropy probability model and the GLRT, that utilizes multiple decision statistics, and we apply this approach using the detection statistics derived from the three clutter models. Examples demonstrate that the joint decision logic can improve detection performance in comparison with the individual anomaly detectors. We also describe the application of linear prediction filters to repeated images of the same area to detect changes that occur within the scene over time. <s> BIB001 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Abstract : We report on the development and application of multispectral infrared imaging technology to problems of interest to the U. S. military. For tactical applications, we show the application of dual-band infrared focal plane arrays to reconnaissance, surveillance, and target acquisition. We describe the development of a new dual-band LWIR/LWIR focal plane array for detection of the disturbed soil features associated with buried land mines. We will describe the application of dual-band FPAs in the strategic defense arena by presenting dual-band MWIR/LWIR imagery of commercial launch vehicles that shows the utility of such an FPA for a sensor in a boost phase interceptor. In addition, we describe the development of acousto-optic tunable filters (AOTF) for hyperspectral imaging in the MWIR and LWIR bands. These devices hold the promise of producing hyperspectral infrared imagers that are considerably smaller and lighter than current systems. The AOTF allows completely electronic spectral scanning for fast access to a discrete set of narrow spectral bands. We will discuss ARL,s efforts in developing a novel scanerless laser radar (LADAR) system based on the frequency modulated continuous wave (FMCW) technique. This `flash" LADAR system uses a focal plane array to image the return signals from chirped pulses from a solid state laser to determine both 2D image and range information about a target. This data can then be used to construct a 3-dimensional picture of the target. Keywords: Infrared, Multispectral, AOTF, Hyperspectral, LADAR. <s> BIB002 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Abstract M. longissimus dorsi minced pork patties from three dietary treatment groups of DLY (Duroc/Landrace/Yorkshire) cross bred pigs were packaged in polythene bags and placed in a retail refrigerated display cabinet at 5 °C±1 °C, under fluorescent light (1000 lux) for up to 5 days. Each dietary treatment group consisted of pigs ( n =7) fed either a low vitamin E diet (80 mg dl -α-tocopheryl acetate/kg of feed), supplemental iron (7 g iron (II) sulphate/kg feed) or supplemental vitamin E (200mg dl -α-tocopheryl acetate/kg of feed) + supplemental iron). Samples were subjected to visual colour evaluation by a trained sensory panel ( n =8) and an untrained panel ( n =8) on days 0, 1, 3 and 5. Instrumental Hunter L ∗ , a ∗ and b ∗ values were measured on each day of analysis using a Minolta colorimeter. In addition RGB (red, green and blue) and Hunter L ∗ , a ∗ and b ∗ values were measured using a digital camera. The use of trained and untrained panellists are both relevant in the visual assessment of meat products. In a previous study O'Sullivan, Byrne, and Martens (2003) indicated that the untrained panellist is analogous to the consumer and how they perceive colour changes in meat. However, the trained panellist is useful in the assessment of unfamiliar products and where a greater degree of discrimination is required. The order of oxidation of the experimental treatments was Control (low vitamin E) L value descriptors. This was postulated to be due to the fact that the camera took measurements over the entire surface of samples and thus a more representative measurement was taken compared to the colorimeter. <s> BIB003 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Applying computer vision in meat quality evaluation has been an active area of research in recent years. Various studies have addressed issues from basic technique development to applications. This paper summarizes the main results from a number of recent application studies, which include characterization of quality attributes such as color, marbling, maturity and texture; prediction of sensory scores and grades; and prediction of cooked-meat tenderness. The promise of computer vision for objective meat quality evaluation is demonstrated and the remaining challenges are discussed. <s> BIB004 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Sixty longissimus (L) muscle chops were selected according to marbling score in order to develop a technique for the quantitative description of marbling fat by means of computer image analysis (CIA) and study its relationship with intramuscular fat content and shear force variation in pork. L muscle samples were taken from gilts belonging to three genetic lines differing in carcass leanness, namely Large White (LW), Meishan-derived dam line (M) and Synthetic Genex 3000 (SG). SG gilts had leaner loins (P<0.001) than LW and M. However, the SG loins were more marbled (P<0.001) and had higher IMF content. In addition, CIA analysis showed that marbling fat was finer and better distributed in the SG line. CIA marbling characteristics were significantly correlated with both IMF content (P<0.001) and shear force values (P<0.05) demonstrating the reliability of this technique to assess the contribution of marbling characteristics to variations in pork eating quality. <s> BIB005 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Highly efficient target detection algorithms in hyperspectral remote sensing technology, particularly for the long range detection of very low observable objects which exhibit extremely small detection cross sections, are in great demand. This is more so for a near or real time application. This paper is concerned with global anomaly detections (GAD), and conventional methods to achieve better detection using multiple approach fusion (MAF), which fuses detection outputs from various detectors using either logical operators, or, via a model based estimation of the joint detection statistics from all detectors, is found to be not good enough. This work emphasises the need to integrate a more comprehensive background modelling into the GAD to develop a robust anomaly detector (AD). Then, the detection output from this detector is fused with other detectors via MAF for a further improvement of detection performance. The MUF2 algorithm is formulated exactly using this 2-level fusion mechanism, in which m ixture modelling and spectral u nmixing f usion have been employed. The significance of background modelling in GAD has been highlighted in this work using real data. The result has shown a factor of 2-5 reduction in detection performance when a very small amount of target pixels (~0.1%) is misclassified as background. This is because anomalies are defined with reference to a model of the background, and subsequently two new background classification techniques have been proposed in this work. The effectiveness of the MUF2 has been assessed using three representative data sets which contain various different types of targets, ranging from vehicles to small plates embedded in backgrounds with various degrees of homogeneity. The performance of MUF2 has been shown to be more superior than the conventional GAD frequently in orders of magnitude, regardless of the background homogeneity and target types. The current version of the MUF2 is run under Matlab and it takes ~2 minutes to process a 20K pixel imagery. ::: This work forms part of the research programme supported by the EMRS DTC established by the UK MOD. <s> BIB006 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Whereas overhead infrared imagery shows promise for detecting buried landmines, detection algorithms must deal with the daunting challenge of distinguishing between landmines and clutter objects which frequently possess similar spatial and spectral characteristics to landmines. However, groups of clutter features are rarely related spatially in the same way that groups of mines are related. For this reason, the recognition of minefield patterns in overhead landmine imagery can be useful to the detection of mines in minefields. In this paper, we present a simple method for detecting grid patterns in imagery, discuss means by which the method may be extended to a more general category of patterns, provide a method for the automated prediction of the locations of undetected mines based upon the observed pattern, and finally we discuss applications. Examples are provided using longwave infrared hyperspectral imagery. <s> BIB007 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated. <s> BIB008 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Characterization of hazardous lands using ground-based techniques can be very challenging. For this reason, airborne ::: surveys are often preferred. The use of thermal infrared imaging represents an interesting approach as surveys can be ::: carried out under various illumination conditions and that the presence of buried objects typically modifies the thermal ::: inertia of their surroundings. In addition, the burial or presence of a buried object will modify the particle size, texture, ::: moisture and mineral content of a small region around it. All these parameters may lead to emissivity contrasts which will ::: make thermal contrast interpretation very challenging. In order to illustrate the potential of airborne thermal infrared ::: hyperspectral imaging for buried object characterization, various metallic objects were buried in a test site prior to an ::: airborne survey. Airborne hyperspectral images were recorded using the targeting acquisition mode, a unique feature of ::: the Telops Hyper-Cam Airborne system which allows recording of successive maps of the same ground area. Temperatureemissivity ::: separation (TES) was carried out on the hyperspectral map obtained upon scene averaging. The thermodynamic ::: temperature map estimated after TES highlights the presence of hot spots within the investigated area. Mineral mapping ::: was carried out upon linear unmixing of the spectral emissivity datacube obtained after TES. The results show how the ::: combination of thermal information and mineral distribution leads to a better characterization of test sites containing buried ::: objects. <s> BIB009 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Hyperspectral images provide great potential for target detection, however, new challenges are also introduced for hyperspectral target detection, resulting that hyperspectral target detection should be treated as a new problem and modeled differently. Many classical detectors are proposed based on the linear mixing model and the sparsity model. However, the former type of model cannot deal well with spectral variability in limited endmembers, and the latter type of model usually treats the target detection as a simple classification problem and pays less attention to the low target probability. In this case, can we find an efficient way to utilize both the high-dimension features behind hyperspectral images and the limited target information to extract small targets? This paper proposes a novel sparsity-based detector named the hybrid sparsity and statistics detector (HSSD) for target detection in hyperspectral imagery, which can effectively deal with the above two problems. The proposed algorithm designs a hypothesis-specific dictionary based on the prior hypotheses for the test pixel, which can avoid the imbalanced number of training samples for a class-specific dictionary. Then, a purification process is employed for the background training samples in order to construct an effective competition between the two hypotheses. Next, a sparse representation-based binary hypothesis model merged with additive Gaussian noise is proposed to represent the image. Finally, a generalized likelihood ratio test is performed to obtain a more robust detection decision than the reconstruction residual-based detection methods. Extensive experimental results with three hyperspectral data sets confirm that the proposed HSSD algorithm clearly outperforms the state-of-the-art target detectors. <s> BIB010 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the complicated conditions of the land-cover distribution. This in turn makes it difficult to obtain an accurate background estimation to distinguish the anomaly targets. The sparse learning technique provides a way to obtain an implicit background representation with the learned dictionary and corresponding sparse codes. In this paper, we explore the background/anomaly information content for each atom of the learned dictionary, from an analysis based on the frequency of the dictionary atoms for HSI reconstruction. From this perspective, we propose a novel sparsity score estimation framework for hyperspectral anomaly detection. First, an overcomplete dictionary and the corresponding sparse code matrix are obtained from the HSI. The frequency of each dictionary atom for reconstruction, which is also called the atom usage probability, is then estimated from the sparse code matrix. Finally, the estimated frequencies are transformed to the sparsity score for each pixel, which can be seen as the degree of “anomalousness.” In the proposed detection framework, two strategies are proposed to enhance the diversity between the background and anomaly information in the learned dictionary: 1) dictionary-based background feature transformation and 2) dictionary iterative reweighting. A series of real-world HSI data sets is utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art anomaly detection methods. <s> BIB011 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> G. DEFENCE AND HOMELAND SECURITY <s> Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods. <s> BIB012
The usage of HSI has quickly spread to various civilian applications and more recently to sectors of defense and homeland security BIB002 . HSI is generally used as a countercountermeasure for detection and recognition of camouflaged targets in military applications. HSI can easily detect strategic deployments in unpopulated areas such as forests, desserts and mountains, where targets such as military vehicles and mines are distinct from the background even if camouflaged. Anomaly detection methods BIB006 - BIB001 that use spectral information to differentiate between targets and background without any prior knowledge have gained significant popularity over the past few years. Yuen and Bishop BIB006 proposed an HSI based anomaly detection algorithms ''MUF2'', which uses a multiple approach fusion methodology. Experiments were performed on images with crops, vegetation and bare soil from the Barrax dataset. An image from the Barrax dataset and the results of target detection using MUF2 algorithm are shown in Figure 11 . An extremely high detection rate of 100% was achieved at a false alarm rate of 2 × 10 −4 . Remote detection of small targets such as mines is a very challenging task. MUF2 algorithm BIB006 was also tested for detection of camouflaged land mines as shown in Figure 12 . Due to the very small area of the target in the images and noise in the dataset, 60% detection accuracy was achieved at a false alarm rate of 3 × 10 −3 . Suganthi and Korah  proposed an Artificial Neural Network (ANN) based technique for landmine detection and segmentation in infrared images. Letalick et al.  tested the possibility and feasibility of using multiple optical sensors and concluded that HSI has high potential in detection and recognition of semi-hidden, hidden and camouflaged landmines if prior knowledge of the spectral signature of the target. However, limited detection rate is achieved when landmines are completely hidden or camouflaged with vegetation. Zhao et al. BIB011 proposed a sparse learning technique for hyperspectral anomaly detection, in which they employed dictionary based transformation of background features and iterative reweighting to exaggerate the differences between anomalies and background. Du et al. BIB010 proposed a hybrid sparsity and statistics anomaly detector to overcome the limitations of sparsity models in hyperspectral images containing spectral variability with limited endmembers. Discriminative metric learning method BIB008 and local geometric structure VOLUME 6, 2018 features BIB012 have also been used for hyperspectral anomaly detection in the recent literature. Thomas et al. BIB007 conducted a research on detection of grid patterns of landmines to improve the efficacy of landmine detection in LWIR hyperspectral imagery. Anomalies are detected using Dual Window based Eigen Separation Transform (DWEST) and pattern parameters are extracted. A pattern projection image is formed using the extracted parameters followed by pattern based reduction of false alarm rate. Higher detection probability at lower false alarm rate was noted which shows that the use of spatial pattern parameters in anomaly detection improves land mine detection BIB007 . Gagnon et al. BIB009 used hyperspectral image analysis to detect buried targets using a LWIR airborne hyperspectral camera. The temperature of disturbed soil over the buried target was found to be higher than the temperature of undisturbed soil around the target area. Classification techniques such as SVM and linear unmixing are used to distinguish between the targets and naturally hot areas. In the 21 st century, development of state-of-the-art technologies for counterterrorism is one of the fastest growing demands. One of the significant ideas proposed for counterterrorism is detecting improvised explosive devices (IED). It is a technical challenge to directly detect explosives packed in airtight and light manner. Contrary to the direct detection of IEDs, cognitive methods use effective computing to analyze the contextual and body language, behavior, gestures, facial expressions and activity to assess the intent of a person. Mental stress is one of the key indicators of threat which is a short term induced neurological imbalance caused by any situation which involves a possible threat or danger. During neurological stress, the sympathetic nervous system induces adrenaline hormones to the blood triggers increase in blood flow to muscles and dilation of pupil. These unavoidable physiological changes during stress are detected as an indication of stress level using HSI BIB004 . Assessment of hemoglobin oxygenation using spectroscopic tuning is proposed in BIB003 and BIB005 . Figure 13 shows the oxy-hemoglobin levels of a person at different levels of stress.