A Semiparametric Model for Hyperspectral Anomaly Detection

Dalton Rosario

doi:10.1155/2012/425947

journal article Open Access Jan 01, 2012

A Semiparametric Model for Hyperspectral Anomaly Detection

Dalton Rosario

Journal of Electrical and Computer Engineering Vol. 2012 No. 1 · Wiley

View at Publisher Save 10.1155/2012/425947

Abstract

Using hyperspectral (HS) technology, this paper introduces an autonomous scene anomaly detection approach based on the asymptotic behavior of a semiparametric model under a multisample 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: (i) 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; (ii) the utility of a semiparematric model, where underlying distributions of spectra are not assumed to be known but related through an exponential function; (iii) derivation of the asymptotic cumulative probability of the approach making mistakes, allowing the user some control of probabilistic errors. Results using real HS data are promising for autonomous manmade object detection in difficult natural clutter backgrounds from two viewing perspectives: nadir and forward looking.

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References

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Published: Jan 01, 2012
Vol/Issue: 2012(1)
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Dalton Rosario

Cite This Article

Dalton Rosario (2012). A Semiparametric Model for Hyperspectral Anomaly Detection. Journal of Electrical and Computer Engineering, 2012(1). https://doi.org/10.1155/2012/425947

A Semiparametric Model for Hyperspectral Anomaly Detection

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