基于光谱信息散度与光谱角匹配的高光谱解混算法

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。

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

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.