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Hyperspectral band selection via optimal neighborhood reconstruction / Qi Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8465 - 8476 Titre : Hyperspectral band selection via optimal neighborhood reconstruction Type de document : Article/Communication Auteurs : Qi Wang, Auteur ; Fahong Zhang, Auteur ; Xuelong Li, Auteur Année de publication : 2020 Article en page(s) : pp 8465 - 8476 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse combinatoire (maths) [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] optimisation (mathématiques) [Termes IGN] reconstruction d'image [Termes IGN] réduction Résumé : (auteur) Band selection is one of the most important technique in the reduction of hyperspectral image (HSI). Different from traditional feature selection problem, an important characteristic of it is that there is usually strong correlation between neighboring bands, that is, bands with close indexes. Aiming to fully exploit this prior information, a novel band selection method called optimal neighborhood reconstruction (ONR) is proposed. In ONR, band selection is considered as a combinatorial optimization problem. It evaluates a band combination by assessing its ability to reconstruct the original data, and applies a noise reducer to minimize the influence of noisy bands. Instead of using some approximate algorithms, ONR exploits a recurrence relation that underlies the optimization target to obtain the optimal solution in an efficient way. Besides, we develop a parameter selection approach to automatically determine the parameter of ONR, ensuring it is adaptable to different data sets. In experiments, ONR is compared with some state-of-the-art methods on six HSI data sets. The results demonstrate that ONR is more effective and robust than the others in most of the cases. Numéro de notice : A2020-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987955 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987955 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96372 [article]