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《Acta Optica Sinica》 2009-09
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Selecting Per-Pixel Endmembers Set Based on Bayesian Inference

Li Xi~1 Guan Zequn~2 Qin Kun~2 Zhang Li~3 Cao Lingling~4 (1 State Key Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan,Hubei 430079,China 2 School of Remote Sensing and Information Engineering,Wuhan University,Wuhan,Hubei 430079,China 3 722nd Research Institute of China Shipbuilding Industry CO,Wuhan,Hubei 430079,China 4 School of Mathematical Sciences,Peking University,Beijing 100871,China)  
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 endmemehers’spectra and the coexistence of endmembers,Bayesian inference can increase the accuracy rate of endmembers’selection,so the unmixing accuracy was improved significantly.
【Fund】: 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
【CateGory Index】: TP751.1
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