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Elliptically Contoured Distributions for Anomalous Change Detection in Hyperspectral Imagery

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4 Author(s)
Theiler, J. ; Los Alamos Nat. Lab., Los Alamos, NM, USA ; Scovel, C. ; Wohlberg, B. ; Foy, B.R.

We derive a class of algorithms for detecting anomalous changes in hyperspectral image pairs by modeling the data with elliptically contoured (EC) distributions. These algorithms are generalizations of well-known detectors that are obtained when the EC function is Gaussian. The performance of these EC-based anomalous change detectors is assessed on real data using both real and simulated changes. In these experiments, the EC-based detectors substantially outperform their Gaussian counterparts.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:7 ,  Issue: 2 )