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Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing / Jaime Zabalza in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 93 (July 2014) . - pp. 112 - 122 Titre : Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing Type de document : Article/Communication Auteurs : Jaime Zabalza, Auteur ; Jinchang Ren, Auteur ; Mingqiang Yang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 112 - 122 Langues : Anglais (eng) Descripteur : [Termes IGN] analyse en composantes principales [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image hyperspectrale [Termes IGN] image radar [Termes IGN] matrice de covariance Résumé : As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used. Numéro de notice : A2014-330 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.04.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73697 [article]

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