NDLI: Kernel-based Linear Spectral Mixture Analysis for hyperspectral image classification

Content Provider	IEEE Xplore Digital Library
Author	Keng-Hao Liu Englin Wong Chein-I Chang
Copyright Year	2009
Description	Author affiliation: Remote Sensing Signal and Image, Processing Laboratory, the Department of Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, USA (Keng-Hao Liu; Englin Wong) \|\| Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250 USA (Chein-I Chang)
Abstract	Linear Spectral Mixture Analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.
Starting Page	1
Ending Page	4
File Size	1084202
Page Count	4
File Format	PDF
ISBN	9781424446865
DOI	10.1109/WHISPERS.2009.5289096
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-26
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral sensors Image resolution Linear spectral unmixing (LSU) Subspace constraints Least squares orthogonal subspace projection (LSOSP) Spectral analysis Least squares methods Remote sensing Image analysis Virtual dimensionality (VD) Fully constrained least squares (FCLS) Non-negativity constrained least squares (NCLS) Kernel Hyperspectral imaging Image classification
Content Type	Text
Resource Type	Article

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