摘要
提出了利用贝叶斯推理选择混合像元内端元的模型。考虑到端元光谱的不确定性,基于贝叶斯推理和线性光谱混合模型得到了像元内端元集合的后验概率表达式。在获得端元共存的先验知识基础上,结合端元光谱的正态分布函数,通过最大后验概率得到最佳的端元集合。通过对包含147431个像元的ETM+影像试验表明,相对于IDRISI软件的MRES和PG算法,该算法可削减至少70%的冗余端元,使端元选择错误导致的分解误差降低至少28%。结果表明,由于充分考虑端元光谱的不确定性和端元的共存性,通过贝叶斯推理可以大幅度提高端元选择的正确率,从而改善混合像元的分解精度。
A Bayesian inference model was developed to select per-pixel endmembers set. Considering the uncertainty of endmembers＇ spectra, based on Bayesian inference and linear spectral mixture model, the posterior probability of per-pixel endmembers set was obtained. Using the prior knowledge of endmembers＇ coexistence, combined with the normal distribution function of endmembers＇ spectra, the optimal endmembers set was selected based on maximal liklihood. The experiment on an ETM＋ image including 147431 pixel showed that, compared with the MRES and PG algorithms provided by IDRISI, the Bayesian approach reduced 70% redundant endmembers, and the unmixing error induced by inaccurate endmembers was reduced to 28 %. The result showed that, considering the uncertainty of endmemebers＂ spectra and the coexistence of endmembers, Bayesian inference can increase the accuracy rate of endmembers＂ selection, so the unmixing accuracy was improved significantly.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2009年第9期2577-2583,共7页
Acta Optica Sinica
基金
国家自然科学基金(60875007)
全国百篇优秀博士论文奖项目(199936)
北京大学为新研究基金(W08SD04)资助课题
关键词
遥感
端元选择
贝叶斯推理
混合像元
端元集合
线性光谱混合模型
remote sensing
endmember selection
Bayesian inference
mixed pixel
endmembers set
linear spectral mixture model
