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Proceedings Paper

Discriminant analysis with nonparametric estimates for subpixel detection of 3D objects in hyperspectral imagery
Author(s): Yong Liu; Glenn Healey
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Paper Abstract

The large amount of spectral information in hyperspectral imagery allows the accurate detection of subpixel objects. The use of subspace models for targets and backgrounds allows detection that is invariant to changing environmental conditions. The non-Gaussian behavior of target and background distribution residuals complicates the development of subspace-based detection methods. In this paper, we use discriminant analysis for feature extraction for separating subpixel 3D objects from cluttered backgrounds. The nonparametric estimation of distributions is used to establish the statistical models using the length and direction of residuals. Candidate subspaces are then evaluated to maximize their discriminatory power which is measured between estimated distributions of targets and backgrounds. In this context, a likelihood ratio test is used based on background and mixed statistics for subpixel detection. The detection algorithm is evaluated for HYDICE images and a number of images simulated using DIRSIG under a variety of conditions. The experimental results demonstrate accurate detection performance on these data sets.

Paper Details

Date Published: 4 May 2006
PDF: 11 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623304 (4 May 2006); doi: 10.1117/12.668224
Show Author Affiliations
Yong Liu, Univ. of California/Irvine (United States)
Glenn Healey, Univ. of California/Irvine (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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