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.
@article{arxiv.1711.08970,
title = {Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery},
author = {Ahmad W. Bitar and Loong-Fah Cheong and Jean-Philippe Ovarlez},
journal= {arXiv preprint arXiv:1711.08970},
year = {2020}
}
Comments
Some wrong information present in the published IEEE version are corrected here (marked in red)