一种考虑光谱变异性的高光谱图像非线性解混算法
Received:January 16, 2018  Revised:February 11, 2018  点此下载全文
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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 
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:非线性解混可以解释高光谱图像复杂场景中的非线性混合效应,但地物的光谱变异性是其中的一个难点。提出一种考虑光谱变异性的无监督非线性解混算法。通过核函数将原始高光谱图像数据隐式地映射到高维特征空间中,从而在该空间中结合光谱变异性进行线性解混;与此同时,依据实际地物的分布特性,添加丰度和光谱变异系数的局部平滑约束。模拟和真实高光谱数据的实验结果表明,该方法能克服不同非线性混合场景中存在的光谱变异性问题,提高光谱解混的精度。
中文关键词:高光谱图像,非线性光谱解混,光谱变异性,核方法,平滑约束
 
A Nonlinear Unmixing Algorithm Dealing with Spectral Variability for Hyperspectral Imagery
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》