约束最小二乘的高光谱图像非线性解混  

Nonlinear unmixing of hyperspectral imagery based on constrained least squares

作　　者：普晗晔[1,2] 王斌[1,2] 夏威[3] 

机构地区：[1]复旦大学电磁波信息科学教育部重点实验室,上海200433 [2]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 [3]中国交通通信信息中心,北京100011

出　　处：《红外与毫米波学报》2014年第5期552-559,共8页Journal of Infrared and Millimeter Waves

基　　金：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(2013-KF-02);国家自然科学基金(41371337);上海市教委科研创新项目(13ZZ005);高等学校博士学科点专项科研基金(20110071110018)~~

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.Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing mod- els, a new nonlinear unmixing algorithm for hyperspectral imagery was proposed. By introducing the abundance nonneg- ative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters, the proposed algo- rithm transforms the hyperspectral unmixing problem into a constrained nonlinear least squares problem. It consists of two sub-problems which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. Then, the alternating iterative optimization technique was used to solve this problem. The experimental results on syn- thetic and real hyperspectral dataset demonstrated that the proposed algorithm can effectively overcome the inherent limi- tations 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.

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 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]