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A vector sift detector for interest point detection in hyperspectral imagery / L. Dorado-Munoz in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4521 - 4533 Titre : A vector sift detector for interest point detection in hyperspectral imagery Type de document : Article/Communication Auteurs : L. Dorado-Munoz, Auteur ; M. Velez-Reys, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 4521 - 4533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données vectorielles [Termes IGN] extraction automatique [Termes IGN] image AISA+ [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] point d'intérêt [Termes IGN] SIFT (algorithme) Résumé : (Auteur) This paper presents an algorithm for the extraction of interest points in hyperspectral images. Interest points are spatial features of the image that capture information from their neighbors, are distinctive and stable under transformations such as translation and rotation, are helpful in data reduction, and reduce the computational burden of various algorithms such as image registration by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. Interest points have been applied to problems in computer vision, including image matching, recognition, 3-D reconstruction, and change detection. Interest point operators for monochromatic images were proposed more than a decade ago and have extensively been studied. An interest point operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, and stable under geometric transformations. An extension of Lowe's scale-invariant feature transform (SIFT) to vector images is proposed here. The approach takes the vectorial nature of the hyperspectral images into account. Furthermore, the multiscale representation of the image is generated by vector nonlinear diffusion, which leads to improved detection, because it better preserves edges in the image as opposed to Gaussian blurring, which is used in Lowe's original approach. Experiments with hyperspectral images of the same and different resolutions that were collected with the Airborne Hyperspectral Imaging System (AISA) and Hyperion sensors are presented. Evaluation of the proposed approach using repeatability criterion and image registration is carried out. Comparisons with other approaches that were described in the literature are presented. Numéro de notice : A2012-591 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2191791 Date de publication en ligne : 09/05/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2191791 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32037 [article]