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)


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.


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.


  1. 1.
    Scharf, L.L., Friedlander, B.: Matched subspace detectors. IEEE Trans. Signal Process 42(8), 2146–2157 (1994)CrossRefGoogle Scholar
  2. 2.
    Harsanyi, J.C., Chang, C.I.: Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sensing 32(4), 779–785 (1994)CrossRefGoogle Scholar
  3. 3.
    Robey, F.C., Fuhrmann, D.R., Kelly, E.J.: A cfar adaptive matched filter detector. IEEE Trans. on Aerospace and Elect. Syst. 28(1), 208–216 (1992)CrossRefGoogle Scholar
  4. 4.
    Kraut, S., Scharf, L.L., McWhorter, T.: Adaptive subspace detectors. IEEE Trans. Signal Process. 49(1), 1–16 (2001)CrossRefGoogle Scholar
  5. 5.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  6. 6.
    Kwon, H., Nasrabadi, N.M.: Kernel matched subspace detectors for hyperspectral target detection. IEEE Trans. Pattern Anal. Machine Intell. 28(2), 178–194 (2006)CrossRefGoogle Scholar
  7. 7.
    Kwon, H., Nasrabadi, N.M.: Kernel orthogonal subspace projection for hyperspectral signal classification. IEEE Trans. Geosci. Remote Sensing 43(12), 2952–2962 (2005)CrossRefGoogle Scholar
  8. 8.
    Kwon, H., Nasrabadi, N.M.: Kernel spectral matched filter for hyperspectral imagery. Int. J. of Computer Vision 71(2), 127–141 (2007)CrossRefGoogle Scholar
  9. 9.
    Kwon, H., Nasrabadi, N.M.: Kernel adaptive subspace detector for hyperspectral target detection.  3(2), 178–194 (2006)Google Scholar

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

Personalised recommendations