Hyperspectral target detection using independent component analysis-based linear mixture model

E. Sarigul; M. S. Alam

doi:10.1117/12.720230

7 May 2007 Hyperspectral target detection using independent component analysis based linear mixture model

E. Sarigul, M. S. Alam

Proceedings Volume 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII; 65650A (2007) https://doi.org/10.1117/12.720230
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

E. Sarigul and 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); https://doi.org/10.1117/12.720230

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