A Novel Spectral-spatial Sparse Method for Hyperspectral Target Detection

doi:10.3969/j.issn.1000-1093.2014.06.013

Abstract

Abstract: Target detection is one of the most important applications of hyperspectral imagery (HSI). The traditional target detection techniques usually discard the spatial information of the target, resulting in a lower accuracy of detection. A novel simultaneous sparse representation model is proposed for HSI target detection. The proposed approach applies the theory and algorithm of mixed-norm to the hyperspectral target detection. By considering the combination of spectral information and spatial context of HSI, a model with a mixed-norm regularizaton based on the simultaneous sparse representation is proposed. And this model is finally solved via alternating direction mehtod of multipliers (ADMM) efficiently. The effectiveness and accuracy of the proposed simultaneous sparse representation model and algorithm are demonstrated by experimental results on a real hyperspectral images.

Key words: information processing, hyperspectral imagery, target detection, mixed norm, simultaneous sparsity, altermation direetion mehtod of multipciers

CLC Number:

TG156

SONG Yi-gang, WU Ze-bin , SUN Le, LIU Jian-jun, WEI Zhi-hui. A Novel Spectral-spatial Sparse Method for Hyperspectral Target Detection[J]. Acta Armamentarii, 2014, 35(6): 834-841.

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