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  • Research Article
  • Open Access

A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery

EURASIP Journal on Advances in Signal Processing20062007:029250

https://doi.org/10.1155/2007/29250

  • Received: 30 September 2005
  • Accepted: 18 May 2006
  • Published:

Abstract

Several linear and nonlinear detection algorithms that are based on spectral matched (subspace) filters are compared. Nonlinear (kernel) versions of these spectral matched detectors are also given and their performance is compared with linear versions. Several well-known matched detectors such as matched subspace detector, orthogonal subspace detector, spectral matched filter, and adaptive subspace detector are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The expression for each detection algorithm is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. Experimental results based on simulated toy examples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.

Keywords

  • Kernel Function
  • Feature Space
  • Matched Filter
  • Orthogonal Subspace
  • Kernel Version

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Authors’ Affiliations

(1)
US Army Research Laboratory, ATTN: AMSRL-SE-SE, 2800 Powder Mill Road, Adelphi, MD 20783-1197, USA

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Copyright

© H. Kwon and N. M. Nasrabadi 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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