Kernel adaptive subspace detector for hyperspectral target detection

In this paper, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that detects signals of interest in a high dimensional (possibly infinite) feature space associated with a certain nonlinear mapping. In order to address the high dimensionality of the feature space, ASD is first implicitly formulated in the feature space which is then converted into an expression in terms of kernel functions via the kernel trick of the Mercer kernels. The proposed kernel-based ASD (KASD) exploits the nonlinear correlations between the spectral bands that is ignored by the conventional ASD. Experimental results based on the given hyperspectral image show that the proposed KASD outperforms the conventional ASD.

[1]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[2]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[3]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[4]  Mark A. Girolami,et al.  Mercer kernel-based clustering in feature space , 2002, IEEE Trans. Neural Networks.

[5]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[6]  Heesung Kwon,et al.  Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[8]  N. Nasrabadi,et al.  Kernel-based subpixel target detection in hyperspectral images , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[9]  L. Scharf,et al.  The CFAR adaptive subspace detector is a scale-invariant GLRT , 1998, Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381).