27 April 2009 A hyperspectral anomaly detector based on partialing out a clutter subspace
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Abstract
An anomaly detector for hyperspectral imaging based on partialling out the effect of the clutter subspace is devised. The partialling maximizes the squared correlation between each spectral component and a linear predictor, with no restrictions on the form of the probability distribution. The detection step is defined by thresholding a Mahalanobis measure of the prediction error. The method is compared to conventional anomaly detectors using VNIR hyperspectral imagery.
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Edisanter Lo, Alan Schaum, "A hyperspectral anomaly detector based on partialing out a clutter subspace", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733404 (27 April 2009); doi: 10.1117/12.821012; https://doi.org/10.1117/12.821012
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