Paper Info
Title
Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery
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.
Year
DOI
Venue
2011
10.1016/j.patrec.2010.09.022
Pattern Recognition Letters
Keywords
Field
DocType
target detection,multidimensional spectral vector space,new spectral,regularized spectral angle,kernel methods,kernel version,kernel-based regularized-angle spectral,spectral matched filter,detection performance,kernel mapping,hyperspectral imagery,conventional spectral,real hyperspectral image,spectral angle mapper,vector space,matched filter,kernel method
Kernel (linear algebra),Computer vision,Vector space,Full spectral imaging,Pattern recognition,Hyperspectral imaging,Spectral method,Artificial intelligence,Matched filter,Kernel method,Detector,Mathematics
Journal
Volume
Issue
ISSN
32
2
Pattern Recognition Letters
Citations 
PageRank 
References 
7
0.50
11
Authors
4
Name
Order
Citations
PageRank
Yanfeng Gu174255.56
Chen Wang21085.96
Shizhe Wang3863.29
Ye Zhang430443.70