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. These kernel-based detectors exploit the nonlinear correlations between the spectral bands that are ignored by the conventional detectors. 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 simulated toyexamples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.


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    Titel :

    Kernel Matched Signal Detectors for Hyperspectral Target Detection


    Beteiligte:
    Heesung Kwon, (Autor:in) / Nasrabadi, N.M. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    2866479 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Kernel Spectral Matched Filter for Hyperspectral Imagery

    Kwon, H. / Nasrabadi, N. M. | British Library Online Contents | 2007


    Hyperspectral Target Detection using Kernel Orthogonal Subspace Projection

    Kwon, H. / Nasrabadi, N. M. | British Library Conference Proceedings | 2005