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Proceedings Paper

Principle of indirect comparison (PIC): simulation and analysis of PIC-based anomaly detection in multispectral data
Author(s): Dalton Rosario
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Paper Abstract

The Army has gained a renewed interest in hyperspectral (HS) imagery for military surveillance. As a result, a HS research team has been established at the Army Research Lab (ARL) to focus exclusively on the design of innovative algorithms for target detection in natural clutter. In 2005 at this symposium, we presented comparison performances between a proposed anomaly detector and existing ones testing real HS data. Herein, we present some insightful results on our general approach using analyses of statistical performances of an additional ARL anomaly detector testing 1500 simulated realizations of model-specific data to shed some light on its effectiveness. Simulated data of increasing background complexity will be used for the analysis, where highly correlated multivariate Gaussian random samples will model homogeneous backgrounds and mixtures of Gaussian will model non-homogeneous backgrounds. Distinct multivariate random samples will model targets, and targets will be added to backgrounds. The principle that led to the design of our detectors employs an indirect sample comparison to test the likelihood that local HS random samples belong to the same population. Let X and Y denote two random samples, and let Z = X U Y, where U denotes the union. We showed that X can be indirectly compared to Y by comparing, instead, Z to Y (or to X). Mathematical implementations of this simple idea have shown a remarkable ability to preserve performance of meaningful detections (e.g., full-pixel targets), while significantly reducing the number of meaningless detections (e.g., transitions of background regions in the scene).

Paper Details

Date Published: 4 May 2006
PDF: 11 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623323 (4 May 2006); doi: 10.1117/12.666081
Show Author Affiliations
Dalton Rosario, Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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