NDLI: Spectral Unmixing in Multiple-Kernel Hilbert Space for Hyperspectral Imagery

Content Provider	IEEE Xplore Digital Library
Author	Yanfeng Gu Shizhe Wang Xiuping Jia
Copyright Year	1980
Abstract	In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3968
Ending Page	3981
Page Count	14
File Size	1388909
File Format	PDF
ISSN	01962892
Volume Number	51
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Hyperspectral imaging Hilbert space Machine learning Materials support vector machines (SVMs) Hyperspectral imagery multiple-kernel learning (MKL) reproducing kernel Hilbert space (RKHS) spectral unmixing
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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