NDLI: A novel nonlinear unmixing scheme for hyperspectral images using the nonlinear least squares technique

Content Provider	IEEE Xplore Digital Library
Author	Hanye Pu Bin Wang Geng-Ming Jiang Jian Qiu Zhang Bo Hu Dan Li
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. Eng., Fudan Univ., Shanghai, China (Hanye Pu; Bin Wang; Jian Qiu Zhang; Bo Hu; Dan Li) \|\| Key Lab. of Wave Scattering & Remote Sensing Inf., Fudan Univ., Shanghai, China (Geng-Ming Jiang)
Abstract	Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing models, this paper proposes a new nonlinear unmixing framework for hyperspectral imagery. The proposed framework transforms the hyperspectral unmixing problem to a constrained nonlinear least squares problem by introducing the abundance nonnegative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters. Accordingly, an alternating iterative optimization algorithm is developed to solve the arising nonlinear least squares problem. The method decomposes the nonlinear unmixing problem into two sub-problems, which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. The experimental results on synthetic and real hyperspectral dataset demonstrate that the proposed algorithm can effectively overcome the inherent limitations of the linear mixing model. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used as an effective technique for the nonlinear unmixing of hyperspectral imagery.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	2547
Ending Page	2550
File Size	321188
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Signal to noise ratio Biological system modeling Runtime Vectors Frequency modulation bound constraint Hyperspectral imagery nonlinear unmixing nonlinear least squares abundance nonnegative constraint abundance sum-to-one constraint
Content Type	Text
Resource Type	Article

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