Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2019

Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery

Résumé

Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (HSI) as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the targets based on a pre-learned target dictionary constructed from some online spectral libraries. Based on the proposed method, two strategies are briefly outlined and evaluated to realize the target detection on both synthetic and real experiments.
Fichier principal
Vignette du fichier
DEMR19044.1557413506_postprint.pdf (4.4 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02134179 , version 1 (20-05-2019)

Identifiants

Citer

Ahmad W. Bitar, Loong-Fah Cheong, Jean-Philippe Ovarlez. Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57 (8), pp.5239-5251. ⟨10.1109/TGRS.2019.2897635⟩. ⟨hal-02134179⟩
37 Consultations
23 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More