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Proceedings Paper

Urchin: an RX-derivative accounting for anisotropies in whitened clutter
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Paper Abstract

The most widespread methods of anomaly detection in hyperspectral imagery (HSI) are the RX algorithm and its variants (e.g. Subspace RX). RX is optimal for any unimodal elliptically contoured distribution (ECD), and in certain data sets, it misinterprets any deviations from this model as true anomalies. Singleton outliers are by definition anomalous, but other RX detections can arise from less severe departures from the ECD, in the form of spectral "prominences." We describe a method that mitigates such persistent false alarms by augmenting RX in a recursive process with truncated versions of the Adaptive Cosine Estimator (ACE). ACE is applied to RX exceedances that arise from prominences, bulges appearing in the whitened clutter distribution that indicate anisotropy. The ACE-augmented RX decision surface resembles a sea urchin.

Paper Details

Date Published: 13 May 2010
PDF: 6 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769504 (13 May 2010); doi: 10.1117/12.850222
Show Author Affiliations
Brian J. Daniel, U.S. Naval Research Lab. (United States)
Alan P. Schaum, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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