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Extraction of spectral channels from hyperspectral images for classification purposes / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 2 (February 2007) . - pp 484 - 495 Titre : Extraction of spectral channels from hyperspectral images for classification purposes Type de document : Article/Communication Auteurs : S.B. Serpico, Auteur ; G. Moser, Auteur Année de publication : 2007 Article en page(s) : pp 484 - 495 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] classification dirigée [Termes IGN] extraction [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) This paper proposes a procedure to extract spectral channels of variable bandwidths and spectral positions from the hyperspectral image in such a way as to optimize the accuracy for a specific classification problem. In particular, each spectral channel ("s-band") is obtained by averaging a group of contiguous channels of the hyperspectral image ("h-bands"). Therefore, if one wants to define m s-bands, the problem can be formulated as the optimization of the related m starting and m ending h-bands. Toward this end, we propose to adopt, as an optimization criterion, an interclass distance computed on a training set and to generate a sequence of possible solutions by one of three possible search strategies. As the proposed formalization of the problem makes it analogous to a feature-selection problem, the proposed three strategies have been derived by modifying three feature-selection strategies, namely: 1) the "sequential forward selection", 2) the "steepest ascent," and 3) the "fast constrained search". Experimental results on a well-known hyperspectral data set confirm the effectiveness of the approach, which yields better results than other widely used methods. The importance of this kind of procedure lies in feature reduction for hyperspectral image classification or in the case-based design of the spectral bands of a programmable sensor. It represents a special case of feature extraction that is expected to be more powerful than feature selection. The kind of transformation used allows the interpretability of the new features (i.e., the spectral bands) to be saved. Copyright IEEE Numéro de notice : A2007-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.886177 En ligne : https://doi.org/10.1109/TGRS.2006.886177 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28446 [article]

Exemplaires(2)

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065-07021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-07022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Partially supervised classification of remote sensing images through SVM-based probability density estimation / P. Mantero in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 559 - 570 Titre : Partially supervised classification of remote sensing images through SVM-based probability density estimation Type de document : Article/Communication Auteurs : P. Mantero, Auteur ; G. Moser, Auteur ; S.B. Serpico, Auteur Année de publication : 2005 Article en page(s) : pp 559 - 570 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] estimation statistique [Termes IGN] probabilités [Termes IGN] réalité de terrain Résumé : (Auteur) A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used. Numéro de notice : A2005-168 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.842022 En ligne : https://doi.org/10.1109/TGRS.2004.842022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27306 [article]

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065-05032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-05031	RAB	Revue	Centre de documentation	En réserve L003	Disponible