Urchin: An RX-derivative accounting foranisotropies in whitened clutter

摘要

The most widespread methods of anomaly detection in hyperspectral imagery (HSI) are the RX algorithm and itsvariants (e.g. Subspace RX). RX is optimal for any unimodal elliptically contoured distribution (ECD), and in certaindata sets, it misinterprets any deviations from this model as true anomalies. Singleton outliers are by definitionanomalous, 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 recursiveprocess with truncated versions of the Adaptive Cosine Estimator (ACE). ACE is applied to RX exceedances that arisefrom prominences, bulges appearing in the whitened clutter distribution that indicate anisotropy. The ACE-augmentedRX decision surface resembles a sea urchin.