Simultaneous sparsity-based binary hypothesis model for real hyperspectral target detection

Ahmad W Bitar,Jean-Philippe Ovarlez,Loong-Fah Cheong

doi:10.1109/icassp.2017.7953031

Abstract

In this paper, a simultaneous sparsity representation-based binary hypothesis (S-SRBBH) model for target detection in hyperspectral image (HSI) is proposed. The S-SRBBH exploits the interpixel correlation within neighboring pixels in HSI, and then, each test pixel is represented by only the background dictionary (A b ) under null hypothesis or from the union of A b and target dictionary (A t ) under alternative hypothesis. Usually, an inner window region (IWR) centered within an outer window region (OWR) contribute in constructing A b . Indeed, the use of IWR has a huge effect on the detection performance since it encloses the targets of interests, but its use requires the information of the size of the targets which is usually hardly available. That is why, this paper also serves to construct A b without IWR by exploiting the low-rank and sparse matrix decomposition (LRaSMD) technique to decompose the HSI into low-rank background HSI and sparse target HSI. Then for each test pixel, a concentric window is located on the low-rank background HSI, and all the pixels within the window contribute to form A b . Two real HSIs are used to demonstrate that S-SRBBH achieves good target detection especially when the LRaSMD technique is exploited to construct A b .

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