Imaging Components, Systems, and Processing

Partitioned correlation model for hyperspectral anomaly detection

[+] Author Affiliations
Edisanter Lo

Susquehanna University, Department of Mathematical Sciences, 514 University Avenue, Selinsgrove, Pennsylvania 17870, United States

Opt. Eng. 54(12), 123114 (Dec 29, 2015). doi:10.1117/1.OE.54.12.123114
History: Received December 12, 2014; Accepted November 24, 2015
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Abstract.  We develop an algorithm based on a subspace model to detect anomalies in a hyperspectral image. The anomaly detector is based on the Mahalanobis distance of a residual from a pixel that is partitioned nonuniformly according to the groups in the spectral components in the pixel. The main background is removed from the pixel by predicting linear combinations of each subset of the partitioned pixel with linear combinations of the main background. The residual is defined to be the difference between the linear combinations of each subset of the partitioned pixel and the linear combinations of the main background. The anomaly detector is designed for anomalies that can be best detected in the residual of the pixel. Experimental results using two real hyperspectral images and a simulated dataset show that the anomaly detector outperforms conventional anomaly detectors.

© 2015 Society of Photo-Optical Instrumentation Engineers

Topics

Sensors ; Matrices

Citation

Edisanter Lo
"Partitioned correlation model for hyperspectral anomaly detection", Opt. Eng. 54(12), 123114 (Dec 29, 2015). ; http://dx.doi.org/10.1117/1.OE.54.12.123114


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