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Remote Sensing Technology and Application  2013, Vol. 28 Issue (4): 731-738    DOI: 10.11873/j.issn.1004-0323.2013.4.731
    
Advances in Nonlinear Spectral Unmixing of Hyperspectral Images
Tang Xiaoyan1,2,Gao Kun1,Ni Guoqiang1
(1.Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education of China,Beijing Institute of Technology,Beijing 100081,China;
2.School of Electronics and Electrical Engineering,Nanyang Institute of Technology,Nanyang 473004,China)
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Abstract  

Due to the limitation of spatial resolution,there are lots of mixed pixels in spaceborne hyperspectral images.Spectral unmixing of hyperspectral images is an important premise for accurate terrain classification and identification.Compared with traditional spectral unmixing techniques based on the linear mixing model,nonlinear spectral unmixing techniques has better performance in finding endmembers and their abundances.The principle of nonlinear spectral mixture is analysed,and nonlinear unmixing algorithms increased in recent years are summarized.This paper emphatically introduces bilinear model,neural networks,nonlinear spectral decomposing based on kernel function and manifold learning.Some future directions of research are introduced.

Key words:  Mixed pixel      Nonlinear unmixing      Bilinear model      Neural networks      Kernel function      Manifold learning     
Received:  11 October 2012      Published:  14 August 2013
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Tang Xiaoyan
Gao Kun
Ni Guoqiang

Cite this article: 

Tang Xiaoyan,Gao Kun,Ni Guoqiang. Advances in Nonlinear Spectral Unmixing of Hyperspectral Images. Remote Sensing Technology and Application, 2013, 28(4): 731-738.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2013.4.731     OR     http://www.rsta.ac.cn/EN/Y2013/V28/I4/731

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