基于光谱信息散度的光谱解混算法

Advanced spectral unmixing algorithm based on spectral information divergence

光谱解混是高光谱遥感定量化的关键,提出了一种基于光谱信息散度和光谱混合分析的光谱解混改进算法(SID-SMA,Spectral Information Divergence-Spectral Mixed A-nalysis).以光谱信息散度判定最优端元子集,端元选择时采用端元的初选和二次选择来提高端元选择的精度,得到较小的丰度估计误差.通过光谱库模拟数据的结果可以看出,SID-SMA的端元选择精度和丰度估计精度要优于其他算法,当信噪比为100∶1时,算法端元选择正确率达到了99.8%,29个端元的丰度估计总误差小于0.1,并且算法的速度较快.

Spectral unmixing is a key issue of quantitative remote sensing. An advanced spectral unmixing algorithm based on per-pixel optimal endmembers selection named spectral information divergence-spectral mixed analysis （SID-SMA） was proposed. It determined the optimal endmembers subset using the criteria of SID and selected endmembers through two selection steps which could improve the precision of endmember selection and obtain small abundance estimation error. The results of simulated data from spectral library indicate that SID-SMA has better precision of endmember selection and abundance estimation. When the signal-tonoise ratio （SNR） is 100： 1, the correct proportion of endmember selection arrives at 99.86% and total abundance error of 29 endmembers is less than 0.1 and the speed of SID-SMA is much faster.