Semantic interpretation of hyperspectral images based on the adaptative reduction of dimensionality

L'imagerie hyperspectrale permet d'acquérir des informations spectrales riches d'une scène dans plusieurs centaines, voire milliers de bandes spectrales étroites et contiguës. Cependant, avec le nombre élevé de bandes spectrales, la forte corrélation inter-bandes spectrales et la redondance de l'information spectro-spatiale, l'interprétation de ces données hyperspectrales massives est l'un des défis majeurs pour la communauté scientifique de la télédétection. Dans ce contexte, le grand défi posé est la réduction du nombre de bandes spectrales inutiles, c'est-à-dire de réduire la redondance et la forte corrélation de bandes spectrales tout en préservant l'information pertinente. Par conséquent, des approches de projection visent à transformer les données hyperspectrales dans un sous-espace réduit en combinant toutes les bandes spectrales originales. En outre, des approches de sélection de bandes tentent à chercher un sous-ensemble de bandes spectrales pertinentes. Dans cette thèse, nous nous intéressons d'abord à la classification d'imagerie hyperspectrale en essayant d'intégrer l'information spectro-spatiale dans la réduction de dimensions pour améliorer la performance de la classification et s'affranchir de la perte de l'information spatiale dans les approches de projection. De ce fait, nous proposons un modèle hybride permettant de préserver l'information spectro-spatiale en exploitant les tenseurs dans l'approche de projection préservant la localité (TLPP) et d'utiliser l'approche de sélection non supervisée de bandes spectrales discriminantes à base de contraintes (CBS). Pour modéliser l'incertitude et l'imperfection entachant ces approches de réduction et les classifieurs, nous proposons une approche évidentielle basée sur la théorie de Dempster-Shafer (DST). Dans un second temps, nous essayons d'étendre le modèle hybride en exploitant des connaissances sémantiques extraites à travers les caractéristiques obtenues par l'approche proposée auparavant TLPP pour enrichir la sélection non supervisée CBS. En effet, l'approche proposée permet de sélectionner des bandes spectrales pertinentes qui sont à la fois informatives, discriminantes, distinctives et peu redondantes. En outre, cette approche sélectionne les bandes discriminantes et distinctives en utilisant la technique de CBS en injectant la sémantique extraite par les techniques d'extraction de connaissances afin de sélectionner d'une manière automatique et adaptative le sous-ensemble optimal de bandes spectrales pertinentes. La performance de notre approche est évaluée en utilisant plusieurs jeux des données hyperspectrales réelles.Hyperspectral imagery allows to acquire a rich spectral information of a scene in several hundred or even thousands of narrow and contiguous spectral bands. However, with the high number of spectral bands, the strong inter-bands spectral correlation and the redundancy of spectro-spatial information, the interpretation of these massive hyperspectral data is one of the major challenges for the remote sensing scientific community. In this context, the major challenge is to reduce the number of unnecessary spectral bands, that is, to reduce the redundancy and high correlation of spectral bands while preserving the relevant information. Therefore, projection approaches aim to transform the hyperspectral data into a reduced subspace by combining all original spectral bands. In addition, band selection approaches attempt to find a subset of relevant spectral bands. In this thesis, firstly we focus on hyperspectral images classification attempting to integrate the spectro-spatial information into dimension reduction in order to improve the classification performance and to overcome the loss of spatial information in projection approaches.Therefore, we propose a hybrid model to preserve the spectro-spatial information exploiting the tensor model in the locality preserving projection approach (TLPP) and to use the constraint band selection (CBS) as unsupervised approach to select the discriminant spectral bands. To model the uncertainty and imperfection of these reduction approaches and classifiers, we propose an evidential approach based on the Dempster-Shafer Theory (DST). In the second step, we try to extend the hybrid model by exploiting the semantic knowledge extracted through the features obtained by the previously proposed approach TLPP to enrich the CBS technique. Indeed, the proposed approach makes it possible to select a relevant spectral bands which are at the same time informative, discriminant, distinctive and not very redundant. In fact, this approach selects the discriminant and distinctive spectral bands using the CBS technique injecting the extracted rules obtained with knowledge extraction techniques to automatically and adaptively select the optimal subset of relevant spectral bands. The performance of our approach is evaluated using several real hyperspectral data

Interprétation sémantique d'images hyperspectrales basée sur la réduction adaptative de dimensionnalité

Hyperspectral imagery allows to acquire a rich spectral information of a scene in several hundred or even thousands of narrow and contiguous spectral bands. However, with the high number of spectral bands, the strong inter-bands spectral correlation and the redundancy of spectro-spatial information, the interpretation of these massive hyperspectral data is one of the major challenges for the remote sensing scientific community. In this context, the major challenge is to reduce the number of unnecessary spectral bands, that is, to reduce the redundancy and high correlation of spectral bands while preserving the relevant information. Therefore, projection approaches aim to transform the hyperspectral data into a reduced subspace by combining all original spectral bands. In addition, band selection approaches attempt to find a subset of relevant spectral bands. In this thesis, firstly we focus on hyperspectral images classification attempting to integrate the spectro-spatial information into dimension reduction in order to improve the classification performance and to overcome the loss of spatial information in projection approaches.Therefore, we propose a hybrid model to preserve the spectro-spatial information exploiting the tensor model in the locality preserving projection approach (TLPP) and to use the constraint band selection (CBS) as unsupervised approach to select the discriminant spectral bands. To model the uncertainty and imperfection of these reduction approaches and classifiers, we propose an evidential approach based on the Dempster-Shafer Theory (DST). In the second step, we try to extend the hybrid model by exploiting the semantic knowledge extracted through the features obtained by the previously proposed approach TLPP to enrich the CBS technique. Indeed, the proposed approach makes it possible to select a relevant spectral bands which are at the same time informative, discriminant, distinctive and not very redundant. In fact, this approach selects the discriminant and distinctive spectral bands using the CBS technique injecting the extracted rules obtained with knowledge extraction techniques to automatically and adaptively select the optimal subset of relevant spectral bands. The performance of our approach is evaluated using several real hyperspectral data.L'imagerie hyperspectrale permet d'acquérir des informations spectrales riches d'une scène dans plusieurs centaines, voire milliers de bandes spectrales étroites et contiguës. Cependant, avec le nombre élevé de bandes spectrales, la forte corrélation inter-bandes spectrales et la redondance de l'information spectro-spatiale, l'interprétation de ces données hyperspectrales massives est l'un des défis majeurs pour la communauté scientifique de la télédétection. Dans ce contexte, le grand défi posé est la réduction du nombre de bandes spectrales inutiles, c'est-à-dire de réduire la redondance et la forte corrélation de bandes spectrales tout en préservant l'information pertinente. Par conséquent, des approches de projection visent à transformer les données hyperspectrales dans un sous-espace réduit en combinant toutes les bandes spectrales originales. En outre, des approches de sélection de bandes tentent à chercher un sous-ensemble de bandes spectrales pertinentes. Dans cette thèse, nous nous intéressons d'abord à la classification d'imagerie hyperspectrale en essayant d'intégrer l'information spectro-spatiale dans la réduction de dimensions pour améliorer la performance de la classification et s'affranchir de la perte de l'information spatiale dans les approches de projection. De ce fait, nous proposons un modèle hybride permettant de préserver l'information spectro-spatiale en exploitant les tenseurs dans l'approche de projection préservant la localité (TLPP) et d'utiliser l'approche de sélection non supervisée de bandes spectrales discriminantes à base de contraintes (CBS). Pour modéliser l'incertitude et l'imperfection entachant ces approches de réduction et les classifieurs, nous proposons une approche évidentielle basée sur la théorie de Dempster-Shafer (DST). Dans un second temps, nous essayons d'étendre le modèle hybride en exploitant des connaissances sémantiques extraites à travers les caractéristiques obtenues par l'approche proposée auparavant TLPP pour enrichir la sélection non supervisée CBS. En effet, l'approche proposée permet de sélectionner des bandes spectrales pertinentes qui sont à la fois informatives, discriminantes, distinctives et peu redondantes. En outre, cette approche sélectionne les bandes discriminantes et distinctives en utilisant la technique de CBS en injectant la sémantique extraite par les techniques d'extraction de connaissances afin de sélectionner d'une manière automatique et adaptative le sous-ensemble optimal de bandes spectrales pertinentes. La performance de notre approche est évaluée en utilisant plusieurs jeux des données hyperspectrales réelles

Deep neural networks-based relevant latent representation learning for hyperspectral image classification

International audienceThe classification of hyperspectral image is a challenging task due to the high dimensional space, with large number of spectral bands, and low number of labeled training samples. To overcome these challenges, we propose a novel methodology for hyperspectral image classification based on multi-view deep neural networks which fuses both spectral and spatial features by using only a small number of labeled samples. Firstly, we process the initial hyperspectral image in order to extract a set of spectral and spatial features. Each spectral vector is the spectral signature of each pixel of the image. The spatial features are extracted using a simple deep autoencoder, which seeks to reduce the high dimensionality of data taking into account the neighborhood region for each pixel. Secondly, we propose a multi-view deep autoencoder model which allows fusing the spectral and spatial features extracted from the hyperspectral image into a joint latent representation space. Finally, a semi-supervised graph convolutional network is trained based on thee fused latent representation space to perform the hyperspectral image classification. The main advantage of the proposed approach is to allow the automatic extraction of relevant information while preserving the spatial and spectral features of data, and improve the classification of hyperspectral images even when the number of labeled samples is low. Experiments are conducted on three real hyperspectral images respectively Indian Pines, Salinas, and Pavia University datasets. Results show that the proposed approach is competitive in classification performances compared to state-of-the-art

Statistical multi-criteria progressive bands selection system for endmembers extraction of hyperspectral image

International audienceThe most challenges problems in hyperspectral images processing are the huge amount of data volume and the high correlation between bands. Bands selection technique is one of the common approaches to overcome these issues in order to deal with many applications. However, there are two main issues arising from bands selection which must be addressed as the amount of required bands and the choice of the optimal criterion needed to be used in selecting bands. To deal with these two issues, this demonstration presents a progressive bands selection, which performs progressive band dimensionality and reduction through band prioritization scores calculated by combining various statistical criteria. We have applied the proposed approach on real hyperspectral data and the obtained results show the effectiveness compared to other criteria used in the progressive bands selection

Historical Document Image Segmentation Combining Deep Learning and Gabor Features

Due to the idiosyncrasies of historical document images (HDI), growing attention over the last decades is being paid for proposing robust HDI analysis solutions. Many research studies have shown that Gabor filters are among the low-level descriptors that best characterize texture information in HDI. On the other side, deep neural networks (DNN) have been successfully used for HDI segmentation. As a consequence, we propose in this paper a HDI segmentation method that is based on combining Gabor features and DNN. The segmentation method focuses on classifying each document image pixel to either graphic, text or background. The novelty of the proposed method lies mainly in feeding a DNN with a Gabor filtered image (obtained by applying specific multichannel Gabor filters) instead of an original image as input. The proposed method is decomposed into three steps: a) filtered image generation using Gabor filters, b) feature learning with stacked autoencoder, and c) image segmentation with 2D U-Net. In order to evaluate its performance, experiments are conducted using two different datasets. The results are reported and compared with those of a recent state-of-the-art method

An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation

International audienceWith the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI

An adaptive semantic dimensionality reduction approach for hyperspectral imagery classification

International audienceHyperspectral imagery (HSI) is widely used for several fields of remote sensing such as agriculture, land cover monitoring, and deforestation. However, the HSI classification is a challenge task due to the large number of spectral bands, unavailability of training samples, and the high correlation inter-bands. To address these challenges, we propose in this work a semantic reduction dimensionality approach based on the principal component analysis (PCA) and mutual information-based band selection (MI). Firstly, we project the original HSI using PCA to obtain a novel subspace with lower dimensions. Using the obtained components, a set of rules can be generated to find the relevant spectral bands based on score contribution coefficient. Moreover, the mutual information (MI) is used to select the spectral bands that contain a higher information based on the entropy criterion. We propose then to exploit the selected bands for HSI classification using SVM technique. Experiment results demonstrate that our proposed approach is effective and perform for HSI classification compared to other dimensionality reduction approaches

SHCNet: A semi-supervised hypergraph convolutional networks based on relevant feature selection for hyperspectral image classification

International audienceHyperspectral imagery classification is a challenging task due to the large number of spectral bands, and low number of labeled samples. To overcome these issues, we propose a novel approach for hyperspectral image classification based on feature selection and semi-supervised hypergraph convolutional network working with small number of labeled samples. Firstly, we propose a new unsupervised feature selection method based on an information theoretic criterion. Relevant spectral features are automatically selected while preserving the physical properties of hyperspectral data. Secondly, we construct a spectro-spatial hypergraph in order to represent the complex relationships between pixels. Finally, we propose a semi-supervised hypergraph convolutional network which integrates local vertex features and hypergraph topology in the convolutional layers. The aim of this step is to preserve the spectro-spatial features and to cope with the high correlation between hypernodes during classification. The main advantage of the proposed approach is to allow the automatic selection of relevant spectral bands while preserving the spatial and spectral features. In addition, by accounting for the relationship between pixels leads to improved classification results even when the number of labeled samples is low. Experiments are conducted on two real hyperspectral images show that the proposed approach reaches competitive good performances, and achieves better classification performances compared to state-of-the-art methods

Monitoring intra-urban changes with Hidden Markov Models using the spatial relationships

International audienceThis paper presents a methodology for integrating a new parameter measuring spatial relationships in the hidden Markov models (HMM) in order to detect, interpret and predict changes in urban areas from satellite images. This approach is divided into three phases: the detection of different spatial relationships in the urban area; the training of a hidden Markov model using Baum-Welch learning algorithm, integrating the changing spatial relationships obtained through the Allen's temporal algebra; the interpretation of changes in urban area and the prediction of future changes. Simulated spatiotemporal changes on synthetic data show the interest of this method for the analysis of spatiotemporal changes of relations between objects. Results allows detection and prediction to be performed from the various time series of images for the observations of spatiotemporal events such as urban expansion. It is therefore reasonable to use this approach to interpret and estimate the movement of the urban area

Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques

International audienceIn this paper, we propose a novel adaptive band selection approach for hyperspectral image semantic interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image semantic interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the semantic interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy. Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band... | Request PDF. Available from: https://www.researchgate.net/publication/323194459_Hyperspectral_Imagery_Semantic_Interpretation_Based_on_Adaptive_Constrained_Band_Selection_and_Knowledge_Extraction_Techniques [accessed Feb 16 2018]