Using nonparametric distribution estimates for subpixel detection of 3D objects

Yong Liu; Glenn E. Healey

doi:10.1117/12.542723

12 August 2004 Using nonparametric distribution estimates for subpixel detection of 3D objects

Yong Liu, Glenn E. Healey

Proceedings Volume 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X; (2004) https://doi.org/10.1117/12.542723
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

Yong Liu and Glenn E. Healey "Using nonparametric distribution estimates for subpixel detection of 3D objects", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); https://doi.org/10.1117/12.542723

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