%0 Journal Article
%T Study on Hybrid Inversion Scheme under Bayesian Network<br>贝叶斯网络支持的地表参数混合反演模式研究
%A QU Yong-hu
%A WANG Jin-di
%A LIU Su-hong
%A WAN Hua-wei
%A ZHOU Hong-min
%A LIN Hao-bo
%A <br>屈永华
%A 王锦地
%A 刘素红
%A 万华伟
%A 周红敏
%A 林皓波
%J 遥感学报
%D 2006
%I 
%X A hybrid inversion scheme for estimating surface variables of vegetation is proposed under Bayesian Network(BNet) theory,and then is used to estimate chlorophyll content of winter wheat leaves(Cab) and Leaf Area Index(LAI) of canopy.A coupled physical model named PROSPECT+SAIL was chosen to generate simulation data set,which means that the SAIL model uses the leaf reflectance and transmittance derived from PROSPECT model to simulate canopy directional reflectance.Results derived from simulation data and SHUNYI Experiment in 2001 data show that both LAI and Cab can be estimated with an appreciated accuracy under the proposed scheme,except that there are about 10% of total points falling into failure inversion.Then an uncertain data handling method,which considers the measured data as the random variables obeying Gaussian distribution,is employed to solve the failure problem.As a result the failure points are removed successfully though the RMSE of estimated the two variables is larger slightly.The presented hybrid inversion scheme is a knowledge-based inferring mechanism in principle,so the updated information content in the inversion process is quantitatively calculated thanks to the concept of entropy introduced from thermodynamics.Contrasting to the conditional entropy,the posteriori entropy calculated according to our proposed probability revision algorithm is not a descending parameter.This property can give some indications in estimating the information content parameters and the currently used data,that is to say,if the data are consistent with the previously derived information of estimated parameters,then there is descending entropy,otherwise,it is ascending.In the last section of this paper,some discussions are presented about the problem on how to estimate and control the information stream,especially when the inversed physical model is nonlinear.
%K spectra library
%K hybrid inversion
%K bayesian network
%K information entropy<br>贝叶斯网络
%K 混合反演
%K 波谱库
%K 信息熵
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=9EF1ECE0F39598DC&yid=37904DC365DD7266&vid=F3090AE9B60B7ED1&iid=CA4FD0336C81A37A&sid=B31275AF3241DB2D&eid=F3583C8E78166B9E&journal_id=1007-4619&journal_name=遥感学报&referenced_num=4&reference_num=17