CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

Eric P. Crist; Brian J. Thelen; David A. Carrara

doi:10.1117/12.328123

16 October 1998 CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

Eric P. Crist, Brian J. Thelen, David A. Carrara

Proceedings Volume 3438, Imaging Spectrometry IV; (1998) https://doi.org/10.1117/12.328123
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

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.

Citation Download Citation

Eric P. Crist, Brian J. Thelen, and David A. Carrara "CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm", Proc. SPIE 3438, Imaging Spectrometry IV, (16 October 1998); https://doi.org/10.1117/12.328123

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