光谱加权协同稀疏和全变差正则化高光谱图像解混  

Spectral Reweighted Collaborative Sparsity and Total Variation Based Hyperspectral Unmixing Method

作　　者：张绍泉 黄志浩 邓承志[1] 李璠[1] 徐晨光[1] 吴朝明 汪胜前[1] ZHANG Shao-quan;HUANG Zhi-hao;DENG Cheng-zhi;LI Fan;XU Chen-guang;WU Zhao-ming;WANG Sheng-qian(Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Institute of Technology,Nanchang,Jiangxi 330099,China)

机构地区：[1]南昌工程学院江西省水信息协同感知与智能处理重点实验室,江西南昌330099

出　　处：《电子学报》2020年第12期2453-2461,共9页Acta Electronica Sinica

基　　金：江西省教育厅科技项目(No.GJJ180962,No.GJJ190956);国家自然科学基金(No.61901208,No.61865012);江西省自然科学基金(No.20192BAB217003,No.20181ACG70022);南昌工程学院2019年度研究生创新计划项目(No.YJSCX20190014)。

摘　　要：针对传统稀疏解混方法对丰度的稀疏性表征不充分及空间信息利用率低等问题,本文在分析迭代加权稀疏解混方法的基础上,提出了一种基于光谱加权协同稀疏和全变差正则化的高光谱解混方法.该方法一方面在协同稀疏解混的基础上引入光谱加权因子进一步刻画丰度系数的行稀疏性,以促进所有像元之间的联合稀疏性;另一方面引入各向异性全变差空间正则化促进图像同质区域的平滑性,以提高解混的准确性.通过交替方向乘子法求解该模型,通过迭代,利用内外部双循环迭代方法对光谱加权因子和丰度系数进行优化.模拟和真实的高光谱数据实验结果均表明本文提出的算法与现有同类算法相比能大幅提高混合像元分解的精度,在稀疏解混方面展现出了巨大的潜力.In this work,we proposed a hyperspectral unmixing method based on the spectrally weighted collaborative sparsity and the total variation,aiming at alleviating the lack of the sparsity of abundance in traditional methods and fully exploiting the spatial information.On the one hand,the spectral factors are utilized to estimate the weights in order to enforce the sparsity of nonzero rows,thus improving the collaborative sparsity among all the pixels.On the other hand,the total variation based spatial regularization is employed to reinforce the smoothness within the homogenous regions,hence improving the accuracy of unmixing.The model is solved by the well-known alternating direction method of multiplier,in which the spectral factor based weights and the abundance coefficients are iteratively optimized using both the internal and external loops.The experimental results obtained from the simulated and the real datasets indicate that the proposed method could significantly improve the performance of unmixing compared to the other state-of-the-art methods.

关 键 词：高光谱图像 稀疏解混 光谱加权协同稀疏 TV正则项 空间信息 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]