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A graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 803 - 817 Titre : A graph-based classification method for hyperspectral images Type de document : Article/Communication Auteurs : J. Bai, Auteur ; S. Xiang, Auteur ; C. Pan, Auteur Année de publication : 2013 Article en page(s) : pp 803 - 817 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme Graph-Cut [Termes IGN] champ aléatoire de Markov [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image hyperspectrale [Termes IGN] segmentation d'image [Termes IGN] signature spectrale Résumé : (Auteur) The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method. Numéro de notice : A2013-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2205002 En ligne : https://doi.org/10.1109/TGRS.2012.2205002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32219 [article]

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