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Proceedings Paper

Using nonparametric distribution estimates for subpixel detection of 3D objects
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Paper Abstract

The accuracy of subpixel detection in hyperspectral imagery degrades with approximation error arising from cluttered backgrounds and complex target objects. In this paper, we develop a non-parametric generalized likelihood ratio (NGLR) statistic for the subpixel detection of 3-D objects that is invariant to the illumination and atmospheric conditions. We construct the target and background subspaces from target models and the image data. The NGLR is established by estimating the conditional probability densities for the background and target hypotheses using subspace residuals. We use DIRSIG to evaluate the performance of NGLR for detecting subpixel 3-D objects composed of multiple materials in varying illumination and atmospheric conditions. NGLR provides accurate detection results that are invariant to the environmental conditions.

Paper Details

Date Published: 12 August 2004
PDF: 6 pages
Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); doi: 10.1117/12.542723
Show Author Affiliations
Yong Liu, Univ. of California/Irvine (United States)
Glenn E. Healey, Univ. of California/Irvine (United States)

Published in SPIE Proceedings Vol. 5425:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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