基于光谱信息散度与光谱角匹配的高光谱解混算法  

Hyperspectral unmixing algorithm based on spectral information divergence and spectral angle mapping

作　　者：刘万军[1] 杨秀红[1] 曲海成[1,2] 孟煜[1] 

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105 [2]哈尔滨工业大学电子与信息工程学院,哈尔滨150006

出　　处：《计算机应用》2015年第3期844-848,共5页journal of Computer Applications

基　　金：国家863计划项目(2012AA12A405);国家自然科学基金资助项目(61172144)

摘　　要：针对采用线性逆卷积(LD)算法进行端元初选过程中,端元子集中存在相似端元光谱,影响解混精度的问题,提出了一种基于光谱信息散度(SID)与光谱角匹配(SAM)算法的端元子集优选光谱解混算法。通过在端元进行二次选择时,采用以光谱信息散度和光谱角(SID-SA)混合法准则作为最相似端元选择的判据,去除相似端元,降低相似端元对解混精度的影响。实验结果表明,基于SID与SAM的高光谱解混算法将重构影像的均方根误差(RMSE)降低到0.010 4,该方法比传统方法提高了端元的选择精度,减少了丰度估计误差,误差分布更加均匀。When using Linear Deconvolution （LD） algorithm in the selection process, endmembers subset has similar endmembers and similar endmembers have an impact on the accuracy of spectral unmixing, a hyperspectral unmixing optimization algorithm based on per-pixel optimal endmember selection named Spectral Information Divergence （SID） and Spectral Angle Mapping （SAM） was proposed. At the end of the second choice, the method adopted Spectral hfformation Divergence mixed with Spectral Angle （SID-SA） rule as the most similar endmember selection criteria, removed the similar eudmembers and reduced the effect of the accuracy by spectral unmixing. The experiment results show that hyperspectral unmixing optimization algorithm based on SID and SAM makes Root Mean Square Error （RMSE） of reconstruction images be reduced to 0. 010 4. This method improves the accuracy of endmember selection in comparison with traditional method, reduces abundance estimation error and error distributes more evenly.

关 键 词：光谱解混 端元选择 去除端元 解混算法 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]