In this paper, we propose a method for separating known 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 known targets based on a pre-learned target dictionary specified by the user. Based on the proposed method, two strategies are outlined and evaluated independently to realize the target detection on both synthetic and real experiments.
Cite
@article{arxiv.1808.06490,
title = {Target And Background Separation in Hyperspectral Imagery for Automatic Target Detection},
author = {Ahmad W. Bitar and Loong-Fah Cheong and Jean-Philippe Ovarlez},
journal= {arXiv preprint arXiv:1808.06490},
year = {2018}
}
Comments
This paper has been submitted to IEEE ICASSP'18 in October 2017 and got accepted for publication in 29 January 2018. The paper has been presented at the conference in Calgary in 18 April 2018. arXiv admin note: substantial text overlap with arXiv:1711.08970