基于光谱信息散度的光谱解混算法

徐 州; 赵慧洁

基于光谱信息散度的光谱解混算法

徐州,
赵慧洁

北京航空航天大学仪器科学与光电工程学院, 北京 100191

基金项目: 中国地质调查局资助项目(1212010816033);国家863计划资助项目(2008AA121102);长江学者和创新团队发展计划资助项目(IRT0705)

详细信息

作者简介:
徐州(1984-),浙江台州人,硕士生,yurenzhou 2000@163.com.

中图分类号: TP 751.1
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- 文章访问数: 3759
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- 被引次数: 0
出版历程
- 收稿日期: 2008-07-21
- 网络出版日期: 2009-09-30

Advanced spectral unmixing algorithm based on spectral information divergence

School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 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 Analysis).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.
- 光谱解混 /
- 光谱信息散度 /
- 端元选择 /
- 丰度估计
Abstract: 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-to-noise 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.
- spectral unmixing /
- spectral information divergence /
- endmember selection /
- abundance estimation

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参考文献(1)

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