13 May 2010 Hyperspectral outlier detector based on conditional distributions
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Abstract
An outlier detection algorithm for hyperspectral imaging based on likelihood ratio test is presented in this article. The null hypothesis tests if a test pixel is from the conditional distribution of the pixel given the background subspace and the alternative hypothesis tests if a test pixel is from the conditional distribution of the pixel given the target subspace. Using principal components for the complementary subspaces, a practical outlier detector is developed and is compared to conventional outlier detectors using a VNIR hyperspectral imagery.
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Edisanter Lo, "Hyperspectral outlier detector based on conditional distributions", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769506 (13 May 2010); doi: 10.1117/12.851486; https://doi.org/10.1117/12.851486
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KEYWORDS
Sensors

Hyperspectral imaging

Detection and tracking algorithms

Target detection

Hyperspectral target detection

Statistical analysis

Sensor performance

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