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A new sparse source separation-based classification approach / M.A. Loghmari in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 6924 - 6936 Titre : A new sparse source separation-based classification approach Type de document : Article/Communication Auteurs : M.A. Loghmari, Auteur ; Mohamed Saber Naceur, Auteur ; Mohamed-Rached Boussema, Auteur Année de publication : 2014 Article en page(s) : pp 6924 - 6936 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] classification non dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] séparation aveugle de source [Termes IGN] traitement du signal Résumé : (Auteur) In many geoscience applications, we have to convert remotely sensed images to ground cover maps. Numerous approaches to extract ground cover information have been developed. Recently, blind source separation (BSS) of remote-sensing data has received significant attention due to its suitability to recover sources when no information is available about the scanned zone, hence the term blind. In the remote-sensing context, associating each source to a significant land cover theme is difficult and constitutes the real challenge of this paper. Many authors have pointed out that BSS is overwhelmingly a question of contrast and diversity. This reasoning motivates this work which takes advantage of both decorrelation and sparsity to propose a two-level novel approach to separate our different land covers called sources. The first separation stage is based on second-order statistics or decorrelation. It gives a suitable representation of the remote-sensing images. However, decorrelation is a natural way of differentiating statistically between sources but is unable to identify and extract finer features with physical meaning. The aim of the second separation stage is to overcome this problem by an increasingly popular and powerful assumption which is the sparse representation. The last leads to good separation because most of the energy in the defined basis, at any time instant, belongs to a single source. This allows the extraction of physical features and the capture of image essential structures. The innovative aspect of this study concerns the development of a new image classification approach that integrates the BSS at the feature extraction level to provide the most relevant sources from remotely sensed images. It can be viewed as an unsupervised classification method. The second-order separation process is used as a preprocessing step to remove the interband correlation which sometimes brings ill effect to image classification. However, the second-order process is unable to uncover the underlying sources. The basic idea behind our approach is that heterogeneous multichannel data provide sparse spectral signatures in addition to sparse spatial morphologies in specified dictionaries. Hence, sparse modeling can be used to disentangle the land covers from observed mixtures. From the sparse representation, the data space is transformed into a feature space composed of mutually exclusive classes. Finally, we will merge these classes at the decision level in order to enhance the semantic capability and the reliability of land cover classification. The effectiveness of the proposed approach was demonstrated by operating two experiments to study respectively the source separation and the image classification capability of the developed approach. The different results on remote-sensing images illustrate the good performance of the new sparse approach and its robustness to noise. These experiments show that the sparse representation enhances the separation quality and allows extracting more easily the essential structures of the scanned zone. The proposed approach offers an interesting solution to the classification process with limited knowledge of ground truth. Numéro de notice : A2014-542 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2305724 En ligne : https://doi.org/10.1109/TGRS.2014.2305724 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74159 [article]

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A spectral and spatial source separation of multispectral images / M.A. Loghmari in IEEE Transactions on geoscience and remote sensing, vol 44 n° 12 (December 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 12 (December 2006) . - pp 3659 - 3673 Titre : A spectral and spatial source separation of multispectral images Type de document : Article/Communication Auteurs : M.A. Loghmari, Auteur ; Mohamed Saber Naceur, Auteur ; Mohamed-Rached Boussema, Auteur Année de publication : 2006 Article en page(s) : pp 3659 - 3673 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification bayesienne [Termes IGN] données multisources [Termes IGN] hétérogénéité [Termes IGN] image multibande [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] séparabilité [Termes IGN] signature spectrale Résumé : (Auteur) This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned zone constitute the sources to separate. Associating each source to a specific significant theme remains the real challenge in the source-separation method applied to satellite images. In fact, multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. This constitutes the first difficulty for sources identification. The second difficulty lies in the heterogeneity of most of the geological and vegetative ground surfaces. In this case, the geometrical projection of a single detector element at the Earth's surface, which is sometimes called the instantaneous field of view, is formed from a mixture of spectral signatures. In such circumstances, the needed information is either not available or not reliable. In this paper, the goal is to establish a new approach based on a two-level source separation (TLSS), which consists of a spectral separation along the different used bands and a spatial separation along neighboring pixels of each image band. The spectral separation has been used prior to the Bayesian approach, and it is based on a second-order statistics approach that exploits the correlation through different spectral bands of the multispectral sensor. The given images are represented according to independent axes that provide more effective representation of the information within the observation images. The spectral separation consists of identifying the sources without resorting to any a priori information, hence the term blind. The obtained source-separation represent the starting point for the Bayesian approach, which is known for its weakness in front of initial conditions. To identify a significant theme for each source, we have to spatially separate each image based on a Bayesian source-separation framework. The proposed approach has the added advantages of the blind source method as well as the Bayesian method. It should give segmented images related to each theme covering the scanned zone, which are the TLSS results of the observation images. Copyright IEEE Numéro de notice : A2006-559 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.882261 En ligne : https://doi.org/10.1109/TGRS.2006.882261 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28282 [article]

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The contribution of the sources separation method in the decomposition of mixed pixels / Mohamed Saber Naceur in IEEE Transactions on geoscience and remote sensing, vol 42 n° 11 (November 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 11 (November 2004) . - pp 2642 - 2653 Titre : The contribution of the sources separation method in the decomposition of mixed pixels Type de document : Article/Communication Auteurs : Mohamed Saber Naceur, Auteur ; M.A. Loghmari, Auteur ; Mohamed-Rached Boussema, Auteur Année de publication : 2004 Article en page(s) : pp 2642 - 2653 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes indépendantes [Termes IGN] bande spectrale [Termes IGN] classification pixellaire [Termes IGN] décomposition d'image [Termes IGN] fusion de données multisource [Termes IGN] occupation du sol [Termes IGN] signature spectrale [Termes IGN] Tunisie [Termes IGN] valeur radiométrique Résumé : (Auteur) In this paper, we propose to prove the importance of the application of blind sources separation methods on remote sensing data. Indeed, satellite images are represented by radiometric values where each one is considered as a mixture of different sources. The primary goal of our research is to hand back the different sources covering the scanned zone. The main constraint to restore these sources is to take our observation images as a mixture of physically independent components. In our work, the independence between the different sources is obtained through two statistical methods. The first method is based on the reduction of the spatial source correlations, and the second one is based on the joint maximization of the fourth-order cumulants. On the opposite of the original multispectral images that are represented according to correlated axes, the source images extracted from the proposed algorithms are represented according to mutually independent axes that allow each source to represent specifically a certain type of land cover. This increases the reliability of the analysis and the interpretation of the scanned zone. The source images obtained from the application of the sources separation method give a more effective representation of the information contained on the observation images. The performance of these source images is investigated through an application for the decomposition of mixed pixels. The originality of our application comes from the determination of the mixing matrix modeling the spectral endmembers based on source filters. These filters model the sensibility of each source channel according to the different spectral bands, which give an interesting information about the spectral theme represented by the corresponding source image. This application shows that the proportions of the different land cover types existing into the pixel are better estimated through the source images than through the original multispectral images. This method could offer an interesting solution to mixed-pixel classification. Numéro de notice : A2004-463 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.834764 En ligne : https://doi.org/10.1109/TGRS.2004.834764 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26983 [article]

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