遥感统计推断理论与应用初探

Tentative Study on Theory and Application of Remote Sensing Statistical Inference

提出一种新的介于遥感分类(定性分析)与遥感反演(定量分析)之间的遥感定性-定量分析方法,即遥感统计推断。遥感统计推断在理论上基于统计光学和概率分布变换,主要研究在一定区域范围内的地物参数的概率分布特征(如土壤的湿度、温度,水体盐度、叶绿素浓度等)如何改变和影响传感器所观测到的光学参数(如地表反射率、水体的遥感反射率等)的概率分布特征(称之为光谱概率分布),以及如何基于传感器观测到的光谱概率分布去反向推断地物参数在此区域内的统计分布特征,从而为刻画地物参数提供相应的定性与定量信息。相比于传统的遥感分类和反演,遥感统计推断的优点在于:(1)能够快速地获取地物参数的全局统计特征,如均值、方差、极值等等,而不需要反演分析每一个像素光谱,这一优点对于当前很多基于大数据、高分辨率的遥感应用尤为重要,因为有些应用部门的管理人员最感兴趣的往往是管理对象(如湖泊、水库、农田)的整体特征,他们并不需要知道在每一个像素处的地物参数的大小;(2)地物的光谱概率分布可以直接用于地物的分类研究,提供了一种有别于基于像素光谱签名特征的传统遥感分类的新分类方法,这种方法提供的是研究区域的整体分类(如一个湖泊的分类)而不是每个像素的分类(如水质的分类);(3)遥感统计推断可以为遥感反演建模提供辅助信息,基于这些信息调整反演模型的函数和/或参数,使得反演模型结果的统计特征与遥感统计推断的结果相匹配。该文简要介绍了遥感统计推断的一些基本概念、原理以及其相较于遥感分类和反演的优势、推断的适用对象和面向推断的遥感数据处理方法,分析了国内主要湖泊水体的光谱概率分布特征,并基于杭州西湖实地采测数据,提出了一种基于自展法(bootstrap-based)的用于推断关键统计分布参数(例如西湖悬浮颗粒浓度的平均值)的简易遥感统计推断方法。

We propose a new qualitative-quantitative remote sensing analytical method,remote sensing statistical inference,which is different from the remote sensing classification(qualitative analysis)and remote sensing inversion(quantitative analysis).Theoretically based on statistical optics and probability distribution transformation,remote sensing statistical inference mainly studies how the probability distributions of the ground parameters(e.g.,soil moisture and temperature,water salinity and chlorophyll concentration,etc.)within the interested area change and affect the probability distributions(called spectral probability distribution,SPD)of the optical parameters(e.g.,surface reflectance,remote sensing reflectance of water bodies,etc.)observed by the remote sensors,and how to infer the statistical distributions of the ground parameters in this region based on the SPDs observed by the sensor,to provide the corresponding qualitative and quantitative information for characterizing the ground parameters.Compared with the traditional remote sensing classification and inversion,the advantages of remote sensing statistical inference are:(1)it can quickly obtain the global statistical characteristics of the ground parameters,such as mean,variance,max/min,etc.,without inversion of each image pixels,which is especially important for many current remote sensing applications based on big data and high-resolution images,because some application department managers are most interested in the overall statistical distributions of the management objects(such as lakes and reservoirs);(2)the observed SPDs can be directly used in the classification study of ground objects,providing a new classification method different from the traditional remote sensing classification based on the signature features of each pixel,which provides the overall classification of the study object(such as the classification of a lake)rather than the classification of each pixel(e.g.,the classification of water quality);(3)remote sensing statistical inference can provide auxiliary information for remote sensing inversion modeling,and based on the inferred information,the functions and/or parameters of inversion modelscan be adjusted so that the statistical characteristics of the inversed results match the inferred results.This paper briefly introduces some basic concepts and principles of remote sensing statistical inference,its advantages over remote sensing classification and inversion,the applicable objects of inference and remote sensing data processing methods for inference,analyzes the characteristics of the spectral probability distribution of major lakes in China,and proposes a bootstrap-based method for inference using the field measurement data of West Lake in Hangzhou.A simple inference method based on the bootstrap-based method is proposed to infer the key statistical distribution parameters,for example,the mean concentration of the suspended particles in West Lake.