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A Semiparametric Model for Hyperspectral Anomaly Detection

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Executive Summary

Using Hyper-Spectral (HS) technology, this paper introduces an autonomous scene anomaly detection approach based on the asymptotic behavior of a semi-parametric model under a multisampling testing and minimum-order statistic scheme. Scene anomaly detection has a wide range of use in remote sensing applications, requiring no specific material signatures. Uniqueness of the approach includes the following: only a small fraction of the HS cube is required to characterize the unknown clutter background, while existing global anomaly detectors require the entire cube; the utility of a semi-parametric model, where underlying distributions of spectra are not assumed to be known but related through an exponential function; derivation of the asymptotic cumulative probability of the approach making mistakes, allowing the user some control of probabilistic errors.

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