摘要
ＢＲＤＦ物理模型通常非常复杂，很难反演，一般都要利用先验知识固定其中一些参数来反演其余的参数。通常大多数人认为应该固定不敏感的参数，但就如何定义一个参数在反演中的敏感度的问题至今未达成共识。李小文和Ｓｔｒａｈｌｅｒ以及王锦地也认为只有那些最敏感与最不确定的参数应该参加反演，而且应用全部观测数据的一部分去反演，但他们未能说清楚怎么定义“最敏感与最不确定”的参数以及如何寻找相应的那一部分观测数据。由于缺少对参数敏感性和不确定性定量的描述，使得ＢＲＤＦ物理模型的反演成为一种“技巧”或“艺术”。我们尝试去建立一种ＢＲＤＦ模型反演的通用的方法，它基于知识的积累和我们称之为多阶段目标决策（ＭＳＤＴ）的反演策略。这一反演策略基于给定观测方向／波段下的不确定性和敏感性矩阵（ＵＳＭ）。它有点类似于牛顿最小化方法中的偏微分矩阵，但有３个明显的区别：１）考虑了初始估计值的不确定性；２）对初始估计值的依赖性小；３）定义中所有的元素有相同的单位，可定量地比较。文中给出了对于ＡＳＡＳ样本利用ＬｉＳｔｒａｈｌｅｒ几何光学模型得出的一个ＵＳＭ，从中可以很明显地看到应该先反演哪些参数，以及应该用哪部分观测值。在更为复杂的采样模式下，可以用?
Physical BRDF models are usually very complex and difficult to invert. We usually need to employ a priori knowledge in this or that way, fix some parameter values and invert some others. Usually most of us agree that non-sensitive parameters should be fixed, but there has not been any consensus on how to define the sensitivity of a parameter in inversion. Li and Strahler, Li and Wang also suggested that only those the most sensitive and most uncertain parameters should be inverted by using a subset of observations, but they failed to spell out how to determine such “most sensitive and most uncertain” parameters and how to find such a subset of observations. This lacking of consensus and quantitative rules makes inversion of physical BRDF models a case-by-case “trick” or an “art but science”.We tried to develop a general framework for BRDF model inversion. It is based on accumulation of knowledge and an inversion strategy which we called Multi-stage, Sample-direction Dependent, Target-decisions (MSDT).Our MSDT inversion strategy is based on an Uncertainty and Sensitivity Matrix (USM) of parameters at given directions/bands of observations. Its definition is somehow analogous to the partial derivative matrix used in Newton methods for minimization, but there are three significant differences: 1) the uncertainty of the initial guess is taken into account; 2) It is less dependent on the initial guess; 3) all elements have the same unit and therefore quantitatively comparable. An example of USM from Li-Strahler GOMS model and ASAS sampling will be presented, and it is obvious from the matrix what parameter should be inverted first, and what subset of observations should be used. In order to expressed the USM clearly for more complex sampling patterns, contours may be used .To demonstrate the MSDT strategy, we used Changping flight data to invert all the 7 parameters in Li-Strahler GOMS model using all the samples at the same time, then invert step by step depending on the USM-based MSDT, the effect is encouraging.
出处
《遥感学报》
EI
CSCD
1997年第S1期121-130,共10页
NATIONAL REMOTE SENSING BULLETIN
