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Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 4 Tome 2 (April 2013) . - pp 2260 - 2275 Titre : Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery Type de document : Article/Communication Auteurs : Ping Zhong, Auteur ; Runsheng Wang, Auteur Année de publication : 2013 Article en page(s) : pp 2260 - 2275 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] spectroscopie Résumé : (Auteur) Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures Numéro de notice : A2013-224 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2209656 En ligne : https://doi.org/10.1109/TGRS.2012.2209656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32362 [article]

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