NDLI: A New Sparsity-Based Band Selection Method for Target Detection of Hyperspectral Image

Content Provider	IEEE Xplore Digital Library
Author	Kang Sun Xiurui Geng Luyan Ji
Copyright Year	2004
Abstract	Band selection (BS) plays an important role in the dimensionality reduction of hyperspectral data. However, as to the existing BS methods, few are specially designed for target detection. In this letter, we combine the target detection and BS process together and put forward a new BS method for target detection, named least absolute shrinkage and selection operator (LASSO)-based BS (LBS). Interestingly, by using a linear regression model with L1 regularization (LASSO model), LBS transforms the discrete BS problem into the continuous optimization problem, which cannot only avoid the complicated subset selection process but also evaluate the importance of all the bands simultaneously. The experiments on real hyperspectral data demonstrate that LBS is a very effective BS method for target detection.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	329
Ending Page	333
Page Count	5
File Size	630127
File Format	PDF
ISSN	1545598X
Volume Number	12
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Hyperspectral imaging Sun Optimization Earth target detection Band selection (BS) constrained energy minimization (CEM) hyperspectral data least absolute shrinkage and selection operator (LASSO)
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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