基于双线性混合模型的高光谱图像非线性光谱解混
投稿时间:2016-10-21  修订日期:2016-12-04  点此下载全文
引用本文:杨 斌,王 斌,吴宗敏.基于双线性混合模型的高光谱图像非线性光谱解混[J].红外与毫米波学报,2018,37(5):631~641].YANG Bin,WANG Bin,WU Zong-Min.Nonlinear spectral unmixing for hyperspectral imagery based on bilinear mixture models[J].J.Infrared Millim.Waves,2018,37(5):631~641.]
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作者单位E-mail
杨 斌 复旦大学 电磁波信息科学教育部重点实验室 15110720039@fudan.edu.cn 
王 斌 复旦大学 电磁波信息科学教育部重点实验室 wangbin@fudan.edu.cn 
吴宗敏 复旦大学 数学科学学院  
基金项目:国家自然科学基金(61572133);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(2015-KF-01)Foundation item: Supported by National Natural Science Foundation of China (Grant No.61572133); Research Fund for the State Key Laboratory of Earth Surface Processes and Resource Ecology (Grant No. 2015-KF-01)
中文摘要:高光谱遥感图像的非线性光谱解混能弥补线性方法难以解释复杂场景中非线性混合效应的不足, 而双线性混合模型及算法是其研究的热点.提出了一种基于双线性混合模型几何特性的光谱解混算法.通过将模型中的非线性混合项表示为一个融合了共同非线性效应的额外端点的线性贡献, 使复杂的双线性混合模型求解转化为简单的线性解混问题.然后结合传统的线性解混算法直接迭代估计正确的丰度.模拟和真实遥感图像数据的实验结果表明, 与其它相关解混方法相比, 该算法能较好地克服共线性效应以及拟合优化过多参数对双线性混合模型求解造成的不利影响, 同时提高了解混的精度和速度.
中文关键词:  键 词: 高光谱遥感,非线性光谱解混,双线性混合模型,丰度估计,单形体
 
Nonlinear spectral unmixing for hyperspectral imagery based on bilinear mixture models
Abstract:Nonlinear spectral unmixing for hyperspectral remote sensing images can overcome the shortage of linear unmixing methods that failing in explaining the nonlinear mixing effect in more complex scenarios. Meanwhile, bilinear mixture models and their corresponding algorithms are the hot topic of related researches. A nonlinear spectral unmixing algorithm based on the geometric characteristics of bilinear mixture models was proposed. By representing the models' nonlinear mixing terms as the linear contribution of one extra vertex concentrating the common nonlinear mixing effect, solving the complex bilinear mixture models was converted to do the simple linear spectral unmixing. Further, a traditional linear spectral unmixing algorithm was adopted to estimate the abundances directly in an iterative way.Experimental results on simulated and real hyperspectral images indicate that the proposed algorithm can overcome the collinearity effect and the adverse impact caused by fitting too many parameters, and improve both unmixing accuracy and computational speed.
keywords:Hyperspectral remote sensing  nonlinear spectral unmixing  bilinear mixture model  abundance estimation  simplex
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