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Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6354 - 6365 Titre : Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Pedram Ghamisi, Auteur ; Javier Plaza, Auteur Année de publication : 2017 Article en page(s) : pp 6354 - 6365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse en composantes principales [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion de données [Termes IGN] Houston (Texas) [Termes IGN] image hyperspectrale [Termes IGN] Trente Résumé : (Auteur) The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available. Numéro de notice : A2017-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2726901 En ligne : https://doi.org/10.1109/TGRS.2017.2726901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88783 [article]

Hyperspectral feature extraction using total variation component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6976 - 6985 Titre : Hyperspectral feature extraction using total variation component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6976 - 6985 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] extraction automatique [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods. Numéro de notice : A2016-922 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2593463 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2593463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83326 [article]