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约束最小二乘的高光谱图像非线性解混
引用本文:普晗晔,王斌,夏威.约束最小二乘的高光谱图像非线性解混[J].红外与毫米波学报,2014,33(5):552-559.
作者姓名:普晗晔  王斌  夏威
作者单位:1. 复旦大学电磁波信息科学教育部重点实验室,上海200433;北京师范大学地表过程与资源生态国家重点实验室,北京100875
2. 中国交通通信信息中心,北京,100011
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);中国博士后科学基金
摘    要:高光谱图像解混是高光谱数据分析的重要研究内容.在现有混合模型的基础上,提出一种新的高光谱图像非线性解混算法.通过在目标函数中引入丰度的非负及和为一约束以及非线性参数的有界约束,该算法将高光谱图像非线性解混问题转化为求解丰度矢量和非线性参数的约束非线性最小二乘问题,继而采用一种交替迭代优化算法求解该问题.仿真和实际高光谱数据的实验结果表明,所提出的算法有效地克服了线性解混的不足,同时具有良好的抗噪声性能,可以作为一种解决高光谱遥感图像非线性解混的有效手段.

关 键 词:高光谱遥感图像  非线性解混  非线性最小二乘  丰度非负约束  丰度和为一约束  有界约束
收稿时间:2013/4/23 0:00:00
修稿时间:5/9/2013 12:00:00 AM

Nonlinear unmixing of hyperspectral imagery based on constrained least squares
PU Han-Ye,WANG Bin and XIA Wei.Nonlinear unmixing of hyperspectral imagery based on constrained least squares[J].Journal of Infrared and Millimeter Waves,2014,33(5):552-559.
Authors:PU Han-Ye  WANG Bin and XIA Wei
Affiliation:Department of Electronic Engineering,Fudan University,Department of Electronic Engineering,Fudan University,China Transport Telecommunications
Abstract:Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing models, this paper proposes a new nonlinear unmixing algorithm for hyperspectral imagery. Through introducing the abundance nonnegative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters, the proposed algorithm transforms the hyperspectral unmixing problem into a constrained nonlinear least squares problem, which consists of two sub-problems which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. In this paper, we use the alternating iterative optimization techniques to solve this problem. The experimental results on synthetic and real hyperspectral dataset demonstrate that the proposed algorithm can effectively overcome the inherent limitations of the linear mixing model. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used as an effective technique for the nonlinear unmixing of hyperspectral imagery.
Keywords:Hyperspectral imagery  nonlinear unmixing  nonlinear least squares  abundance nonnegative constraint  abundance sum-to-one constraint  bound constraint
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