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Proceedings Paper

Random sampling statistical analysis for adaptive target-scale-invariant hyperspectral anomaly detection
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Paper Abstract

Ground to ground, sensor to object viewing perspective presents a major challenge for autonomous window based object detection, since object scales at this viewing perspective cannot be approximated. In this paper, we present a fully autonomous parallel approach to address this challenge. Using hyperspectral (HS) imagery as input, the approach features a random sampling stage, which does not require secondary information (range) about the targets; a parallel process is introduced to mitigate the inclusion by chance of target samples into clutter background classes during random sampling; and a fusion of results. The probability of sampling targets by chance within the parallel processes is modeled by the binomial distribution family, which can assist on tradeoff decisions. Since this approach relies on the effectiveness of its core algorithmic detection technique, we also propose a compact test statistic for anomaly detection, which is based on a principle of indirect comparison. This detection technique has shown to preserve meaningful detections (genuine anomalies in the scene) while significantly reducing the number of false positives (e.g. transitions of background regions). To capture the influence of parametric changes using both the binomial distribution family and actual HS imagery, we conducted a series of rigid statistical experiments and present the results in this paper.

Paper Details

Date Published: 7 May 2007
PDF: 9 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656522 (7 May 2007); doi: 10.1117/12.719082
Show Author Affiliations
João M. Romano, U.S. Army Armament Research and Development Ctr. (United States)
Dalton Rosario, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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