Anomaly detection within hyperspectral images often relies on the critical step of thresholding to declare the specific pixels based on their anomaly scores. When the detector is built upon sound statistical assumptions, this threshold is often probabilistically based, such as the RX detector and the chi-squared threshold. However, when either the detector lacking statistical framework or the background pixels of the image violate the required assumptions, the approach to thresholding is complicated and can resolve into performance instability. We present a method to test the sensitivity thresholding to small changes in the characteristics of the anomalies based on their Mahalanobis distance to the background class. In doing so, we highlight issues in detectors thresholding techniques comparing statistical approaches against heuristic methods of thresholding.
Adam J. Messer and Kenneth W. Bauer, "Method of sensitivity analysis in anomaly detection algorithms for hyperspectral images," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980U (Presented at SPIE Defense + Security: April 12, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2260965.
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