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Superpixel-based intrinsic image decomposition of hyperspectral images / Xudong Jin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4285 - 4295 Titre : Superpixel-based intrinsic image decomposition of hyperspectral images Type de document : Article/Communication Auteurs : Xudong Jin, Auteur ; Yanfeng Gu, Auteur Année de publication : 2017 Article en page(s) : pp 4285 - 4295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification pixellaire [Termes IGN] décomposition d'image [Termes IGN] décomposition du pixel [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] méthode de réduction d'énergie Résumé : (Auteur) In this paper, we propose a novel superpixel-based intrinsic image decomposition (SIID) framework for hyperspectral images. Intrinsic images are usually referred to the separation of shading and reflectance components from an input image. Considering the high dimensionality of hyperspectral images, we further decompose the shading component into the product of environment illumination and surface orientation changes, thus modeling the problem more properly. The proposed method consists of the following steps. First, we build two superpixel segmentation maps of different scales, i.e., a finer one that is oversegmented and a coarser one that is undersegmented. Based on the observation that the finer superpixel map achieves a higher segmentation accuracy, whereas the coarser superpixel map tends to reserve the objectness of the original image, we model the SIID decomposition problem in a matrix form based on the finer superpixel map and define a constraint matrix by integrating the information in the coarser superpixel map. The constraint matrix is introduced as a secondary constraint in order to make the ill-posed IID problem solvable. Finally, we transform the original decomposition problem into minimizing the Frobenius norm of the proposed matrix energy function and iteratively derive the solution. Our experimental results demonstrate that the proposed method is able to achieve a performance outperforming the state-of-the-art while making a great improvement in efficiency. Numéro de notice : A2017-493 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2690445 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2690445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86423 [article]