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Extended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
[article]
Titre : Extended random walker-based classification of hyperspectral images Type de document : Article/Communication Auteurs : Xudong Kang, Auteur ; Shutao Li, Auteur ; Leyuan Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 144 - 153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) This paper introduces a novel spectral-spatial classification method for hyperspectral images based on extended random walkers (ERWs), which consists of two main steps. First, a widely used pixelwise classifier, i.e., the support vector machine (SVM), is adopted to obtain classification probability maps for a hyperspectral image, which reflect the probabilities that each hyperspectral pixel belongs to different classes. Then, the obtained pixelwise probability maps are optimized with the ERW algorithm that encodes the spatial information of the hyperspectral image in a weighted graph. Specifically, the class of a test pixel is determined based on three factors, i.e., the pixelwise statistics information learned by a SVM classifier, the spatial correlation among adjacent pixels modeled by the weights of graph edges, and the connectedness between the training and test samples modeled by random walkers. Since the three factors are all well considered in the ERW-based global optimization framework, the proposed method shows very good classification performances for three widely used real hyperspectral data sets even when the number of training samples is relatively small. Numéro de notice : A2015-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2319373 En ligne : https://doi.org/10.1109/TGRS.2014.2319373 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75111
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 144 - 153[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
[article]
Titre : Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation Type de document : Article/Communication Auteurs : Zhaohui Xue, Auteur ; Jun Li, Auteur ; Liang Cheng, Auteur ; Peijun Du, Auteur Année de publication : 2015 Article en page(s) : pp 70 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectraleRésumé : (Auteur) This paper presents a new spectral-spatial classification method for hyperspectral images via morphological component analysis-based image separation rationale in sparse representation. The method consists of three main steps. First, the high-dimensional spectral domain of hyperspectral images is reduced into a low-dimensional feature domain by using minimum noise fraction (MNF). Second, the proposed separation method is acted on each features to generate the morphological components (MCs), i.e., the content and texture components. To this end, the dictionaries for these two components are built by using local curvelet and Gabor wavelet transforms within the randomly chosen image partitions. Then, sparse coding of one of the MCs and update of the associated dictionary are sequentially performed with the other one fixed. To better direct the separation process, an undecimated Haar wavelet with soft threshold is performed for the content component to make it smooth. This process is repeated until some stopping criterion is met. Finally, a support vector machine is adopted to obtain the classification maps based on the MCs. The experimental results with hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed scheme provides better performance when compared with other widely used methods. Numéro de notice : A2015-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2318332 En ligne : https://doi.org/10.1109/TGRS.2014.2318332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75110
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 70 - 84[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Manifold-based sparse representation for hyperspectral image classification / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)
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Titre : Manifold-based sparse representation for hyperspectral image classification Type de document : Article/Communication Auteurs : Yuan Yan Tang, Auteur ; Haoliang Yuan, Auteur ; Luoqing Li, Auteur Année de publication : 2014 Article en page(s) : pp 7606 - 7618 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] représentation multipleRésumé : (Auteur) A sparsity-based model has led to interesting results in hyperspectral image (HSI) classification. Sparse representation from a test sample is used to identify the class label. However, an ℓ1-based sparse algorithm sometimes yields unstable sparse representation. Inspired by recent progress in manifold learning, two manifold-based sparse representation algorithms are proposed to exploit the local structure of the test samples in corresponding sparse representations for enforcing smoothness across neighboring samples' sparse representations. Using techniques from regularization and local invariance, two manifold-based regularization terms are incorporated into the ℓ1-based objective function. Extensive experiments show that our proposed algorithms obtain excellent classification performance on three classic HSIs. Numéro de notice : A2014-637 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2315209 En ligne : https://doi.org/10.1109/TGRS.2014.2315209 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75053
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7606 - 7618[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014121 RAB Revue Centre de documentation En réserve L003 Disponible Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)
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Titre : Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Shutao Li, Auteur ; Xudong Kang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7738 - 7749 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse multiéchelle
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] représentation parcimonieuseRésumé : (Auteur) Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers. Numéro de notice : A2014-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2317499 En ligne : https://doi.org/10.1109/TGRS.2014.2317499 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75076
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7738 - 7749[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014121 RAB Revue Centre de documentation En réserve L003 Disponible Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation / Davood Akbari in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)
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Titre : Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation Type de document : Article/Communication Auteurs : Davood Akbari, Auteur ; Abdolreza Safari, Auteur ; Saeid Homayouni, Auteur Année de publication : 2014 Article en page(s) : pp 963 - 970 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification orientée objet
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation hiérarchique
[Termes IGN] zone urbaineRésumé : (auteur)An effective approach to spectral-spatial classification has been achieved using Hierarchical SEGmentation (HSEG) by Tarabalka et al. (2009 and 2010). Our goal is to improve this approach to the classification of hyperspectral images in urban areas. The first step of our proposed method is to segment the spectral images using a novel marker-based HSEG, method. The spatial features from segmented images are then extracted. Spatial information such as the area, entropy, shape, adjacency, and relation features constitute the components of feature space. Last, using both spectral and spatial features, the image objects are classified by a support vector machine (SVM) classifier. Three image data-sets were used to test this method. The results of our experiment indicate that the main advantage of the proposed method, compared to the previous HSEG-based approach, is that it increases classification accuracy by selecting the appropriate contextual features of different objects. Numéro de notice : A2014-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.963 En ligne : https://doi.org/10.14358/PERS.80.10.963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75153
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 963 - 970[article]Approche de détermination de signature de texture : application à la classification de couverts forestiers d’image satellitaire à haute résolution / Wala Zaaboub in Revue Française de Photogrammétrie et de Télédétection, n° 207 (Juillet 2014)PermalinkNoise-signal index threshold: a new noise-reduction technique for generation of reference spectra and efficient hyperspectral image classification / K. Kusuma in Geocarto international, vol 25 n° 7 (November 2010)PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)PermalinkClassification non supervisée d'image RSO à l'aide d'extremums locaux d'histogramme : applications à la cartographie de la mangrove littorale camerounaise / J. Fotsing in Revue Française de Photogrammétrie et de Télédétection, n° 189 (Mars 2008)PermalinkTowards 3D map generation from digital aerial images / L. Zebelin in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 6 (September 2006)PermalinkUrban land-use classification using variogram-based analysis with an aerial photograph / S.S. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 7 (July 2006)PermalinkIntegrating imaging spectroscopy (445-2543nm) and geographic information systems for post-disaster management: a case of hailstorm damage in Sydney / S. Bhaskaran in International Journal of Remote Sensing IJRS, vol 25 n° 13 (July 2004)PermalinkInventaire et suivi des zones humides par télédétection / L. Puente (2004)PermalinkApport de l'imagerie hyperfréquence en classification automatique des images satellite / A. Hamouda (1986)PermalinkClassification des paysages par statistique locale de l'occupation du sol : Méthode de généralisation thématique et cartographique / Hervé Le Men (1982)Permalink