Centre de documentation
scientifique

Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1604 - 1617 Titre : Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary Type de document : Article/Communication Auteurs : Yubin Niu, Auteur ; Bin Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1604 - 1617 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification spectrale [Termes IGN] détection de cible [Termes IGN] image hyperspectrale Résumé : (Auteur) The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Numéro de notice : A2017-158 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2628085 En ligne : https://doi.org/10.1109/TGRS.2016.2628085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84695 [article]