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

被引：20

作者：

Gu, Yanfeng ^{[1
]}

Wang, Chen ^{[1
]}

Wang, Shizhe ^{[1
]}

Zhang, Ye ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150006, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2011年 / 32卷 / 02期

关键词：

Hyperspectral imagery; Target detection; Spectral matched filter; Spectral angle mapper; Kernel methods; ALGORITHMS; FILTER;

D O I：

10.1016/j.patrec.2010.09.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 (C) 2010 Elsevier B V All rights reserved

引用

页码：114 / 119

页数：6

共 19 条

[11] Detection algorithms for hyperspectral Imaging applications [J].

Manolakis, D ;

Shaw, G .

IEEE SIGNAL PROCESSING MAGAZINE, 2002, 19 (01) :29-43

[12]

Manolakis D., 2009, P SPIE, V7334

[13] An introduction to kernel-based learning algorithms [J].

Müller, KR ;

Mika, S ;

Rätsch, G ;

Tsuda, K ;

Schölkopf, B .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (02) :181-201

[14] Regularized spectral matched filter for target recognition in hyperspectral imagery [J].

Nasrabadi, N. M. .

IEEE SIGNAL PROCESSING LETTERS, 2008, 15 (317-320) :317-320

[15]

Nasrabadi NM, 2005, IEEE INT C AC SPEECH, V4, piv/665

[16]

Philippe Lagachene, 2008, REMOTE SENS ENVIRON, V112, P825

[17]

Richards J.A., 2006, Remote Sensing Digital Image Analysis: An Introduction, Vfourth

[18] A CFAR ADAPTIVE MATCHED-FILTER DETECTOR [J].

ROBEY, FC ;

FUHRMANN, DR ;

KELLY, EJ ;

NITZBERG, R .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1992, 28 (01) :208-216

[19] Nonlinear component analysis as a kernel eigenvalue problem [J].

Scholkopf, B ;

Smola, A ;

Muller, KR .

NEURAL COMPUTATION, 1998, 10 (05) :1299-1319

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