NDLI: Kernel sparse representation for hyperspectral target detection

Content Provider	IEEE Xplore Digital Library
Author	Yi Chen Nasrabadi, N.M. Tran, T.D.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA (Yi Chen; Tran, T.D.) \|\| US Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USA (Nasrabadi, N.M.)
Abstract	In this paper, we present a nonlinear kernel-based target detection algorithm for hyperspectral images. The proposed approach relies on the sparse representation of an unknown sample with respect to both background and target training samples in a high-dimensional feature space induced by a kernel function. The sparse representation vector can be recovered via a kernelized greedy algorithm, where the kernel trick is used to avoid explicit evaluations of the data in the feature space. The spatial smoothness in hyperspectral images is also taken into account through a kernelized joint sparsity model. The detection decision is then made by comparing the reconstruction accuracy in terms of the background and target sub-dictionaries. The detection algorithm in a high-dimensional feature space implicitly exploits the higher-order structure (correlations) within the data which cannot be captured by a linear model. Therefore, projecting the pixels into a kernel feature space and kernelizing the linear sparse representation model improves the separability between the background and target classes, leading to a more accurate detection performance.
Starting Page	7484
Ending Page	7487
File Size	301195
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351901
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Detectors Training Joints Vectors Hyperspectral imaging Object detection
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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