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Laplacian eigenmaps-based polarimetric dimensionality reduction for SAR image classification / S.T. Tu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 1 (January 2012) . - pp 170 - 179 Titre : Laplacian eigenmaps-based polarimetric dimensionality reduction for SAR image classification Type de document : Article/Communication Auteurs : S.T. Tu, Auteur ; J.H. Chen, Auteur ; W. Yang, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 170 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar moirée [Termes IGN] occupation du sol [Termes IGN] polarimétrie radar Résumé : (Auteur) In this paper, we propose a novel scheme of polarimetric synthetic aperture radar (PolSAR) image classification. We apply Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique, to a high-dimensional polarimetric feature representation for PolSAR land-cover classification. A wide variety of polarimetric signatures are chosen to construct a high-dimensional polarimetric manifold which can be mapped into the most compact low-dimensional structure by manifold-based dimensionality reduction techniques. This NDR technique is employed to obtain a low-dimensional intrinsic feature vector by the LE algorithm, which is beneficial to PolSAR land-cover classification owing to its local preserving property. The effectiveness of our PolSAR land-cover classification scheme with LE intrinsic feature vector is demonstrated with the RadarSat-2 C-band PolSAR data set and the 38th Research Institute of China Electronics Technology Group Corporation X-band PolInSAR data set. The performance of our method is measured by the separability in the feature space and the accuracy of classification. Comparisons on the feature space show that the LE intrinsic feature vector is more separable than different original feature vectors. Our LE intrinsic feature vector also improves the classification accuracy. Numéro de notice : A2012-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2168532 Date de publication en ligne : 26/10/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2168532 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31481 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012011	RAB	Revue	Centre de documentation	En réserve L003	Disponible