高光谱遥感图像非线性解混研究综述
Received:March 31, 2016  Revised:September 27, 2016  点此下载全文
引用本文:杨 斌,王 斌.高光谱遥感图像非线性解混研究综述[J].Journal of Infrared and Millimeter Waves,2017,36(2):173~185
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Author NameAffiliationE-mail
YANG Bin Key Laboratory for Information Science of Electromagnetic Waves MoE,Fudan University 15110720039@fudan.edu.cn 
WANG Bin Key Laboratory for Information Science of Electromagnetic Waves MoE,Fudan University wangbin@fudan.edu.cn 
基金项目:本研究受国家自然科学基金项目(编号61572133)和北京师范大学地表过程与资源生态国家重点实验室开放基金项目(编号2015-KF-01)资助
中文摘要:高光谱遥感图像广泛存在混合像元问题,需对其进行解混以提高应用精度。目前大多数光谱解混算法主要基于线性光谱混合模型,但是该模型存在着难以解释许多真实地物场景中非线性混合效应的缺陷。为使解混结果的端元及丰度更加精确,需要考虑非线性光谱混合模型及相关算法。文中介绍了近年来非线性光谱解混方法的发展状况,主要包括两类典型非线性混合场景:矿物沙地地区的紧密混合模型和植被覆盖区域的多层次混合模型,以及基于这些模型的非线性解混算法和利用核函数、流形学习等方法的数据驱动非线性光谱解混算法及非线性探测算法。最后分析总结了现有非线性解混模型与算法的优势与缺陷及未来的研究趋势。
中文关键词:高光谱遥感,混合像元,非线性光谱解混,Hapke模型,双线性混合模型,核方法,流形学习
 
Review of Nonlinear Unmixing for Hyperspectral Remote Sensing Imagery
Abstract:Spectral unmixing for hyperspectral remote sensing images is always required due to the existence of mixed pixels. However, most spectral unmixing algorithms at present are proposed based on the linear mixture model which may be invalid in many real-world scenarios with nonlinear spectral mixing effects. Therefore, nonlinear mixture models and their corresponding algorithms should be considered to improve the accuracy of endmember extraction and abundance estimation. This paper aims to introduce the recent advances in nonlinear spectral unmixing models and algorithms focusing on two main typical nonlinear mixing scenarios: intimate mineral mixtures and multilayer mixtures in vegetation covered areas. Further, data-driven nonlinear spectral unmixing algorithms such as kernel methods and manifold learning are also presented here. Finally, both advantages and defects of these models and algorithms are summarized and future research trends are analyzed.
keywords:Hyperspectral remote sensing  mixed pixel  nonlinear spectral unmixing  Hapke model  bilinear mixture model  kernel method  manifold learning
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