在遥感反演中,通常假设反演参数和模型误差的先验分布服从正态分布,这个假设通常不太符合实际。为此,本文提出由Bootstrap方法估计反演参数和模型误差的先验分布的方案。同时对先验数据按照地物分类,统计假设检验表明将先验知识分类的合理性。最后,以RossThick-LiTransit核组合的线性核驱动BRDF模型为例,用NOAA-AVHRR观测数据对使用Bootstrap方法的反演算法进行试验,并与正态假设下的Tikhonov正则化反演和Bayes反演结果比较,说明对先验知识分类和使用Bootstrap方法的遥感反演方法能明显减小参数反演结果的不确定性,提高其可信度。

It is usually assumed that the prior distributions of parameters and error are Gaussian distribution in remote sensing inversion.This assumption seems to be impractical in many cases.Prior distribution of parameters and error are very important in remote sensing inversion since many remote sensing inversion strategies take advantage of prior knowledge.We present a bootstrap method for estimating the prior distributions of parameters and error in this paper.This method relaxes the distribution assumption of parameters and error,and obtains those approximately exact distributions by means of prior data.Moreover,we classify prior data since they are collected from different classes,and implement statistical test for classified prior data.Results show that proper classification of prior data is reasonable.Finally,we take RossThick-LiTransit linear kernel-driven model as an example,and make a comparison of our method with usual Tikhonov regularizing inversion and Bayes inversion under normal hypothesis with NOAA-AVHRR observations.The result shows that classifying prior data and using the prior distribution obtained by bootstrap method can significantly decrease uncertainty of parameters.