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Proceedings Paper

A hyperspectral anomaly detector based on partitioning pixel into adjacent components
Author(s): Edisanter Lo
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Paper Abstract

Detection of anomalous objects in a large scene is an important application of hyperspectral imaging in remote sensing. Current algorithms for anomaly detection are based on partialling out the main background structure from each spectral component of a pixel from a hyperspectral image. The Maximized Subspace Model (MSM) detector has the best probability of detection in comparison with the other anomaly detectors that are based on this model. This paper proposes an anomaly detection algorithm that is based on a more general model than the MSM detector. The anomaly detector is also defined as the Mahalanobis distance of the resulting residual. Experimental results show that the anomaly detector has a substantial improvement in detection over the conventional anomaly detectors.

Paper Details

Date Published: 18 May 2013
PDF: 5 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874303 (18 May 2013); doi: 10.1117/12.2017911
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)


Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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