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Optical Engineering

Partitioned correlation model for hyperspectral anomaly detection
Author(s): Edisanter Lo
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Paper 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.

Paper Details

Date Published: 29 December 2015
PDF: 19 pages
Opt. Eng. 54(12) 123114 doi: 10.1117/1.OE.54.12.123114
Published in: Optical Engineering Volume 54, Issue 12
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)


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