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Superpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2575 - 2588 Titre : Superpixel-based multitask learning framework for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Bin Deng, Auteur ; Jiasong Zhu, Auteur ; Xiuping Jia, Auteur Année de publication : 2017 Article en page(s) : pp 2575 - 2588 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] filtre de Gabor [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) Due to the high spectral dimensionality of hyperspectral images as well as the difficult and time-consuming process of collecting sufficient labeled samples in practice, the small sample size scenario is one crucial problem and a challenging issue for hyperspectral image classification. Fortunately, the structure information of materials, reflecting region of homogeneity in the spatial domain, offers an invaluable complement to the spectral information. Assuming some spatial regularity and locality of surface materials, it is reasonable to segment the image into different homogeneous parts in advance, called superpixel, which can be used to improve the classification performance. In this paper, a superpixel-based multitask learning framework has been proposed for hyperspectral image classification. Specifically, a set of 2-D Gabor filters are first applied to hyperspectral images to extract discriminative features. Meanwhile, a superpixel map is generated from the hyperspectral images. Second, a superpixel-based spatial-spectral Schroedinger eigenmaps (S4E) method is adopted to effectively reduce the dimensions of each extracted Gabor cube. Finally, the classification is carried out by a support vector machine (SVM)-based multitask learning framework. The proposed approach is thus termed Gabor S4E and SVM-based multitask learning (GS4E-MTLSVM). A series of experiments is conducted on three real hyperspectral image data sets to demonstrate the effectiveness of the proposed GS4E-MTLSVM approach. The experimental results show that the performance of the proposed GS4E-MTLSVM is better than those of several state-of-the-art methods, while the computational complexity has been greatly reduced, compared with the pixel-based spatial-spectral Schroedinger eigenmaps method. Numéro de notice : A2017-466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2647815 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86389 [article]

Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2442 - 2459 Titre : Deep supervised and contractive neural network for SAR image classification Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Hongyu Wang, Auteur ; Jianchao Fan, Auteur ; Xiaorui Ma, Auteur Année de publication : 2017 Article en page(s) : pp 2442 - 2459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] algorithme Graph-Cut [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] filtre de Gabor [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] matrice de co-occurrence [Termes descripteurs IGN] niveau de gris (image) Résumé : (Auteur) The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches. Numéro de notice : A2017-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2645226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84748 [article]

Discriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification / Lin He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1381 - 1395 Titre : Discriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification Type de document : Article/Communication Auteurs : Lin He, Auteur ; Jun Li, Auteur ; Antonio J. Plaza, Auteur ; Yuanqing Li, Auteur Année de publication : 2017 Article en page(s) : pp 1381 - 1395 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] filtre de Gabor [Termes descripteurs IGN] filtre passe-bas [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] performance Résumé : (Auteur) Spectral-spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-rank Gabor filtering (DLRGF) method for spectral-spatial hyperspectral image classification. A main innovation of the proposed approach is that our implementation is accomplished by decomposing the standard 3-D spectral-spatial Gabor filter into eight subfilters, which correspond to different combinations of low-pass and bandpass single-rank filters. Then, we show that only one of the subfilters (i.e., the one that performs low-pass spatial filtering and bandpass spectral filtering) is actually appropriate to extract suitable features based on the characteristics of hyperspectral images. This allows us to perform spectral-spatial classification in a highly discriminative and computationally efficient way, by significantly decreasing the computational complexity (from cubic to linear order) compared with the 3-D spectral-spatial Gabor filter. In order to theoretically prove the discriminative ability of the selected subfilter, we derive an overall classification risk bound to evaluate the discriminating abilities of the features provided by the different subfilters. Our experimental results, conducted using different hyperspectral images, indicate that the proposed DLRGF method exhibits significant improvements in terms of classification accuracy and computational performance when compared with the 3-D spectral-spatial Gabor filter and other state-of-the-art spectral-spatial classification methods. Numéro de notice : A2017-154 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2623742 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84689 [article]

Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field / Miaozhong Xu in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)  

Public [article]  inGeocarto international > vol 30 n° 9 - 10 (October - November 2015) . - pp 997 - 1015 Titre : Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field Type de document : Article/Communication Auteurs : Miaozhong Xu, Auteur ; Ming Cong, Auteur ; Tianpeng Xie, Auteur Année de publication : 2015 Article en page(s) : pp 997 - 1015 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] filtre de Gabor [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] transformation en ondelettes Résumé : (Auteur) Here, we describe an unsupervised segmentation method incorporating log-Gabor (LG) filters and a Markov random field (MRF) model for high-resolution (HR) remote sensing (RS) images, based on classical models of the visual receptive field. LG filters were utilised to model the receptive fields of the simple cells in the primary visual cortex and extract detailed features from HR–RS images followed by construction of image pyramid through wavelet decomposition to simulate the hierarchical structure of the visual sensing system. Finally, based on the original HR–RS images, their detailed features and the image pyramid, the MRF image segmentation model was applied to obtain the final segmentation result. Real HR–RS images were used as experimental data to validate the proposed method, both qualitatively (visually) and numerically (with the overall accuracy and Kappa index).The experimental results indicate that the proposed method is effective, feasible and robust to noise. Numéro de notice : A2015-627 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1006529 date de publication en ligne : 26/02/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1006529 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78110 [article]

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Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3681 - 3693 Titre : Local binary patterns and extreme learning machine for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Chen Chen, Auteur ; Hongjun Su, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 3681 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtre de Gabor [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] texture d'image Résumé : (Auteur) It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives. Numéro de notice : A2015-316 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76566 [article]

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Gabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation / L. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)

Permalink

Information content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)  

Permalink

Sparse representation of GPR traces with application to signal classification / Wenbin Shao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Permalink

Traitements numériques des images de télédétection, Vol. 2. Visualisation, techniques d'amélioration de la visualisation des images numériques / Olivier de Joinville (2012)

Permalink

Delineation and geometric modeling of road networks / C. Poullis in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 2 (March - April 2010)

Permalink

Traitement des données de télédétection / Michel-Claude Girard (2010)

Permalink

Traitement statistique du signal / M. Barret (2009)

Permalink

Texture feature fusion with neighborhood oscillating tabu search for high resolution image classification / L. Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)

Permalink

Kalman filtering, theory and practice using MATLAB, [Third edition] / Mohinder S. Grewal (2008)

Permalink

SPOT 5 pour la détection d'urbanisation / V. Lacroix in Revue Française de Photogrammétrie et de Télédétection, n° 178 (Septembre 2005)

Permalink

Fundamentals of radar signal processing / M. Richards (2005)

Permalink

A hybrid texture segmentation method for mapping urban land use / N.N. Kachouie in Geomatica, vol 58 n° 1 (March 2004)

Permalink

Image numérique couleur, de l'acquisition au traitement / Alain Trémeau (2004)

Permalink

Traitement des données de télédétection / Michel-Claude Girard (2004)

Permalink

Wavelets in geodesy and geodynamics / W. Keller (2004)

Permalink

Hyperspectral texture recognition using a multiscale opponent representation / M. Shi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 5 (May 2003)

Permalink

Wavelet triangulated irregular networks / J. Wu in International journal of geographical information science IJGIS, vol 17 n° 3 (may 2003)

Permalink

Introduction au traitement d'images / G. Burel (2001)

Permalink

Modex / Stéphane Mallat (1998)

Permalink