Share Email Print

Proceedings Paper

CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm
Author(s): Eric P. Crist; Brian J. Thelen; David A. Carrara
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

Paper Details

Date Published: 16 October 1998
PDF: 8 pages
Proc. SPIE 3438, Imaging Spectrometry IV, (16 October 1998); doi: 10.1117/12.328123
Show Author Affiliations
Eric P. Crist, ERIM International, Inc. (United States)
Brian J. Thelen, ERIM International, Inc. (United States)
David A. Carrara, ERIM International, Inc. (United States)

Published in SPIE Proceedings Vol. 3438:
Imaging Spectrometry IV
Michael R. Descour; Sylvia S. Shen, Editor(s)

© SPIE. Terms of Use
Back to Top