7 May 2007 Hyperspectral target detection using independent component analysis based linear mixture model
Author Affiliations +
Abstract
This paper presents an Independent Component Analysis (ICA) based linear unmixing algorithm for target detection application. ICA is a relatively new method that attempts to separate statistically independent sources from a mixed dataset. The developed algorithm contains two steps. In the first step, ICA based linear unmixing is used to discriminate statistically independent sources to determine end-members in a given dataset as well as their corresponding abundance images. In the second step, unmixing results are analyzed to identify abundance images that correspond to the target class. The performance of the developed algorithm has been evaluated with several real life hyperspectral image datasets.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. Sarigul, M. S. Alam, "Hyperspectral target detection using independent component analysis based linear mixture model", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65650A (7 May 2007); doi: 10.1117/12.720230; https://doi.org/10.1117/12.720230
PROCEEDINGS
11 PAGES


SHARE
Back to Top