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Proceedings Paper

Local background estimation and the replacement target model
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Paper Abstract

We investigate the detection of opaque targets in cluttered multi/hyper-spectral imagery, using a local background estimation model. Unlike transparent "additive-model" targets (like gas-phase plumes), these are solid "replacement-model" targets, which means that the observed spectrum is a linear combination of the target signature and the background signature. Pixels with stronger targets are associated with correspondingly weaker backgrounds, and background estimators can over-estimate the background in a target pixel. In particular, "subtracting the background" (which generalizes the usual notion of subtracting the mean) to produce a residual image can actually have deleterious effect. We examine an adaptive partial background subtraction scheme, and evaluate its utility for the detection of replacement-model targets.

Paper Details

Date Published: 5 May 2017
PDF: 10 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980V (5 May 2017); doi: 10.1117/12.2262833
Show Author Affiliations
James Theiler, Los Alamos National Lab. (United States)
Amanda Ziemann, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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