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Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 1099 - 4109 Titre : Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification Type de document : Article/Communication Auteurs : Li Ma, Auteur ; Jing Tian, Auteur Année de publication : 2010 Article en page(s) : pp 1099 - 4109 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] image AVIRIS [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale Résumé : (Auteur) Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE. Numéro de notice : A2010-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2055876 Date de publication en ligne : 23/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2055876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30672 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Multiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 4122 - 4132 Titre : Multiple Spectral–Spatial Classification Approach for Hyperspectral Data Type de document : Article/Communication Auteurs : Yuliya Tarabalka, Auteur ; Jon Atli Benediktsson, Auteur ; Jocelyn Chanussot, Auteur ; James C. Tilton, Auteur Année de publication : 2010 Article en page(s) : pp 4122 - 4132 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification multibande [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] segmentation d'image Résumé : (Auteur) A new multiple-classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region with a corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker-selection procedure, each of them combining the results of a pixelwise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectral-spatial classification map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies when compared with previously proposed classification techniques. Numéro de notice : A2010-480 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2062526 Date de publication en ligne : 13/09/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2062526 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30673 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010111	RAB	Revue	Centre de documentation	En réserve L003	Disponible