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Joint sparse representation and multitask learning for hyperspectral target detection / Yuxiang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 894 - 906 Titre : Joint sparse representation and multitask learning for hyperspectral target detection Type de document : Article/Communication Auteurs : Yuxiang Zhang, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur ; Tongliang Liu, Auteur Année de publication : 2017 Article en page(s) : pp 894 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] détection de cible [Termes IGN] image hyperspectrale [Termes IGN] représentation parcimonieuse Résumé : (Auteur) With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods. Numéro de notice : A2017-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2616649 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84632 [article]