Kernel-Based Spectral Matched Signal Detectors for Hyperspectral Target Detection

  • Nasser M. Nasrabadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

In this paper, we compare several detection algorithms that are based on spectral matched (subspace) filters. Nonlinear (kernel) versions of these spectral matched (subspace) detectors are also discussed and their performance is compared with the linear versions. Several well-known matched detectors, such as matched subspace detector, orthogonal subspace detector, spectral matched filter and adaptive subspace detector (adaptive cosine estimator) are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The 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 real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.

Keywords

Feature Space High Dimensional Feature Space Constant False Alarm Rate Kernel Trick Generalize Likelihood Ratio Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Nasser M. Nasrabadi
    • 1
  1. 1.U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783USA

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