Target and Background Separation in Hyperspectral Imagery for Automatic Target Detection

Ahmad W. Bitar; Loong-Fah Cheong; Jean Philippe Ovarlez

ICASSP 2018accepted0 citations

Target and Background Separation in Hyperspectral Imagery for Automatic Target Detection

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

Abstract

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.

BibTeX

@inproceedings{icassp2018_targetandbackgro,
  title = {Target and Background Separation in Hyperspectral Imagery for Automatic Target Detection},
  author = {Ahmad W. Bitar and Loong-Fah Cheong and Jean Philippe Ovarlez},
  booktitle = {ICASSP 2018},
  year = {2018}
}