基于光谱信息散度与光谱角匹配的高光谱解混算法

doi:10.11772/j.issn.1001-9081.2015.03.844

计算机应用 ›› 2015, Vol. 35 ›› Issue (3): 844-848.DOI: 10.11772/j.issn.1001-9081.2015.03.844

基于光谱信息散度与光谱角匹配的高光谱解混算法

刘万军¹, 杨秀红¹, 曲海成^1,2, 孟煜¹

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

收稿日期:2014-09-28 修回日期:2014-10-22 出版日期:2015-03-10 发布日期:2015-03-13
通讯作者: 杨秀红
作者简介:刘万军(1959-),男,辽宁北镇人,教授,主要研究方向:数字图像处理、运动目标检测与跟踪;杨秀红(1990-),女,江苏徐州人,硕士研究生,主要研究方向:高光谱解混;曲海成(1981-),男,山东烟台人,讲师,博士研究生,主要研究方向:遥感影像高性能计算;孟煜(1990-),男,河北唐山人,硕士研究生,主要研究方向:图形图像处理、目标识别与跟踪
基金资助:
国家863计划项目(2012AA12A405);国家自然科学基金资助项目(61172144)

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

LIU Wanjun¹, YANG Xiuhong¹, QU Haicheng^1,2, MENG Yu¹

1. School of Software, Liaoning Technical University, Huludao Liaoning 125105, China;
2. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150006, China

Received:2014-09-28 Revised:2014-10-22 Online:2015-03-10 Published:2015-03-13

摘要/Abstract

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0.0104,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。

关键词: 光谱解混, 端元选择, 去除端元, 解混算法

Abstract:

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 Information Divergence mixed with Spectral Angle (SID-SA) rule as the most similar endmember selection criteria, removed the similar endmembers 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 0.0104. This method improves the accuracy of endmember selection in comparison with traditional method, reduces abundance estimation error and error distributes more evenly.

Key words: spectral unmixing, endmember selection, endmember removal, unmixing algorithm

中图分类号:

TP751.1

刘万军, 杨秀红, 曲海成, 孟煜. 基于光谱信息散度与光谱角匹配的高光谱解混算法[J]. 计算机应用, 2015, 35(3): 844-848.

LIU Wanjun, YANG Xiuhong, QU Haicheng, MENG Yu. Hyperspectral unmixing algorithm based on spectral information divergence and spectral angle mapping[J]. Journal of Computer Applications, 2015, 35(3): 844-848.

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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

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