We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.
The image retrieval frameworks can either be content-based or text-based, depending upon the nature of the query data. The applications of CBIR in RS mainly gained its motion after the Landsat thematic mapper multi-spectral satellite data were made accessible. Subsequently, several techniques came up which focus on training of the retrieval models through the notion of active learning by incorporating the user's knowledge during training iterations @cite_13 . As opposed to the real-valued feature representations, several techniques consider the binary features for quicker response during retrieval. In this regard, @cite_12 proposes a hashing-based approximate nearest neighbor search for a fast and scalable RS image retrieval. Visual databases targeted by CBIR applications have been extended for multi-label image retrieval @cite_7 @cite_6 .
@cite_13 @cite_7 @cite_12 @cite_6
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User-defined classes in large generalist image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors. The interactive retrieval of such image classes is then very difficult. To addess his challenge, we propose and evaluate here two general mprovements of SVM-based relevance feedback methods. First, to optimize the transfer of information between the user and the system, we focus on the criterion employed by the system for selecting the images presented to the user at every feedback round. We put forward a new active learning selection criterion that minimizes redundancy between the candidate images shown to the user. Second, for image classes having very different scales, we find that a high sensitivity of the SVM to the scale of the data brings about a low retrieval performance. We then argue that insensitivity to scale is desirable in this context and we show how to obtain it by the use of specific kernel functions. The experimental evaluation of both ranking and classification performance on several image databases confirms the effectiveness of our selection criterion and of the use of kernels that reduce the sensitivity of SVMs to the scale of the data Conventional supervised content-based remote sensing (RS) image retrieval systems require a large number of already annotated images to train a classifier for obtaining high retrieval accuracy. Most systems assume that each training image is annotated by a single label associated to the most significant semantic content of the image. However, this assumption does not fit well with the complexity of RS images, where an image might have multiple land-cover classes (i.e., multilabels). Moreover, annotating images with multilabels is costly and time consuming. To address these issues, in this paper, we introduce a semisupervised graph-theoretic method in the framework of multilabel RS image retrieval problems. The proposed method is based on four main steps. The first step segments each image in the archive and extracts the features of each region. The second step constructs an image neighborhood graph and uses a correlated label propagation algorithm to automatically assign a set of labels to each image in the archive by exploiting only a small number of training images annotated with multilabels. The third step associates class labels with image regions by a novel region labeling strategy, whereas the final step retrieves the images similar to a given query image by a subgraph matching strategy. Experiments carried out on an archive of aerial images show the effectiveness of the proposed method when compared with the state-of-the-art RS content-based image retrieval methods. Large-scale remote sensing (RS) image search and retrieval have recently attracted great attention, due to the rapid evolution of satellite systems, that results in a sharp growing of image archives. An exhaustive search through linear scan from such archives is time demanding and not scalable in operational applications. To overcome such a problem, this paper introduces hashing-based approximate nearest neighbor search for fast and accurate image search and retrieval in large RS data archives. The hashing aims at mapping high-dimensional image feature vectors into compact binary hash codes, which are indexed into a hash table that enables real-time search and accurate retrieval. Such binary hash codes can also significantly reduce the amount of memory required for storing the RS images in the auxiliary archives. In particular, in this paper, we introduce in RS two kernel-based nonlinear hashing methods. The first hashing method defines hash functions in the kernel space by using only unlabeled images, while the second method leverages on the semantic similarity extracted by annotated images to describe much distinctive hash functions in the kernel space. The effectiveness of considered hashing methods is analyzed in terms of RS image retrieval accuracy and retrieval time. Experiments carried out on an archive of aerial images point out that the presented hashing methods are much faster, while keeping a similar (or even higher) retrieval accuracy, than those typically used in RS, which exploit an exact nearest neighbor search.