Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery

Authors:
Yanfeng Gu;Chen Wang;Shizhe Wang;Ye Zhang
Affiliations:
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China;School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China;School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China;School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
Venue:
Pattern Recognition Letters
Year:
2011

Citing 2
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Target detection based on a dynamic subspace

Pattern Recognition

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Abstract

Target detection is one of the most important applications of hyperspectral imagery in the field of both civilian and military. In this letter, we firstly propose a new spectral matching method for target detection in hyperspectral imagery, which utilizes a pre-whitening procedure and defines a regularized spectral angle between the spectra of the test sample and the targets. The regularized spectral angle, which possesses explicit geometric sense in multidimensional spectral vector space, indicates a measure to make the target detection more effective. Furthermore Kernel realization of the Angle-Regularized Spectral Matching (KAR-SM, based on kernel mapping) improves detection even more. To demonstrate the detection performance of the proposed method and its kernel version, experiments are conducted on real hyperspectral images. The experimental tests show that the proposed detector outperforms the conventional spectral matched filter and its kernel version.