A Nonlinear Unmixing Algorithm Dealing with Spectral Variability for Hyperspectral Imagery
Received:January 16, 2018  Revised:February 11, 2018  download
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Author NameAffiliationE-mail
ZHI Tong-Xiang Key Laboratory for Information Science of Electromagnetic Waves MoE,Fudan University 15210720044@fudan.edu.cn 
YANG Bin Key Laboratory for Information Science of Electromagnetic Waves MoE,Fudan University  
WANG Bin Key Laboratory for Information Science of Electromagnetic Waves MoE,Fudan University wangbin@fudan.edu.cn 
Abstract:Nonlinear unmixing can explain the nonlinear mixing effect in complex scenarios of hyperspectral imagery, but the spectral variability of ground objects is one of the difficulties. An unsupervised nonlinear unmixing algorithm dealing with spectral variability is proposed in this paper. The original hyperspectral image data is implicitly mapped into a high-dimensional feature space through a kernel function and then linear unmixing is applied for hyperspectral imagery in combination with spectral variability in this space. Further, local smoothness constraint is added on abundances and coefficients of spectral variability according to the distribution characteristics of ground objects. Experimental results on simulated and real hyperspectral data indicate that, the proposed algorithm can overcome the spectral variability problem in different nonlinear mixing scenarios and improve the unmixing accuracy.
keywords:Hyperspectral imagery  nonlinear spectral unmixing  spectral variability  kernel function  smoothness
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Copyright:《Journal of Infrared And Millimeter Waves》