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遥感技术与应用  2013, Vol. 28 Issue (4): 731-738    DOI: 10.11873/j.issn.1004-0323.2013.4.731
遥感应用     
高光谱图像非线性解混方法的研究进展
唐晓燕1,2,高昆1,倪国强1
(1.北京理工大学光电成像技术与系统教育部重点实验室,北京 100081;
2.南阳理工学院电子与电气工程学院,河南 南阳 473004)
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)
 全文: PDF(1212 KB)  
摘要:

由于空间分辨率的限制,高光谱遥感图像中存在大量混合像元,对混合像元的解混是实现地物精确分类和识别的前提。与传统的线性解混方法相比,非线性解混方法在寻找组成混合像元的端元以及每个端元的丰度时具有较高的精度。分析了光谱非线性混合的原理,总结了近年来提出的非线性解混算法,重点对双线性模型、神经网络、基于核函数的非线性解混算法以及基于流形学习的非线性解混算法进行了介绍和分析。最后总结了混合像元非线性解混未来发展的趋势。

关键词: 混合像元非线性解混双线性模型神经网络核函数流形学习     
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
收稿日期: 2012-10-11 出版日期: 2013-08-14
:  TP 75  
基金资助:

国防科技重点实验室基金 (J20110502)和航空基金(20100112002)联合资助项目。

通讯作者: 高昆(1974-),男,河南信阳人,副教授,主要从事实时数字图像处理研究。E-mail:gaokun@bit.edu.cn。   
作者简介: 唐晓燕(1979-),女,河南南阳人,博士研究生,主要从事遥感图像处理研究。E-mail:tangxy97@sina.com。
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引用本文:

唐晓燕,高昆,倪国强. 高光谱图像非线性解混方法的研究进展[J]. 遥感技术与应用, 2013, 28(4): 731-738.

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

链接本文:

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

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