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遥感反演中先验知识空间传播模式及验证

发表时间:2008-01-15  浏览量:1901  下载量:821
全部作者: 屈永华,王锦地
作者单位: 北京师范大学/中科院遥感应用研究所遥感科学国家重点实验室;北京师范大学地理学与遥感科学学院
摘 要: 遥感地表参数反演面临的一个难题是遥感数据提供的信息不能完全支持参数反演,在遥感反演中引入先验知识来弥补遥感数据的信息不足在遥感反演界已取得共识。本文基于贝叶斯网络参数反演方法,建立了先验知识在遥感像元尺度上的空间传播模式。在贝叶斯反演中,参数的后验信息吸收了已有的先验知识以及遥感数据提供的新的信息,将此后验分布作为空间相邻像元反演时的先验知识即构成了本文提出的先验知识空间传播模式的核心思想。针对遥感反演中存在的诸多不确定性因素,本文提出了基于概率贴近度的概念并用来对反演的结果进行了验证。
关 键 词: 遥感信息工程;空间传播模式;先验知识;反演;不确定性
Title: A prior knowledge spatial spread model in remote sensing inversion
Author: QU Yonghua, WANG Jindi
Organization: Beijing Normal University/China State key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography Beijing Normal University
Abstract: In the general method to retrieve land surface parameters while inverting a physical-based model, only the remotely sensed data is employed. However, it should be noted that the information contained in the adjacent pixels may provide some information that may be used to help retrieving target parameters. In this article, a priori knowledge spread model is proposed to conduct the issue of spatial information spread while retrieving vegetation key parameters(Leaf Area Index, LAI) using a Bayesian network method in regional scale. In our proposed algorithm, the posterior distribution of target parameters is used as the prior knowledge for its adjacent points. When using the posterior information as the spatial adjacent points’ prior information, the spatial correlation information has been added into the process of inversion, thus the inversion process is the spread of priori knowledge and new data information. In this paper, this algorithm of spatial spread of prior knowledge is abbreviated as SSPK.To validate our proposed method, the field measured data as well as ETM+ Imagery collected in Shunyi, Beijing in China dated on 17 April 2001 is employed. The experiment’s objects are focus on the winter wheat when derived the data set. Using these data, the canopy LAI (Leaf Area Index) of winter wheat is estimated using our proposed model and validated using part of the collected data. The estimated result of LAI in this region has lower RMSE for two parts of region, one is 0.53 and another is 0.66.The validation result shows that using the probability likelihood as the complementary measurement index of estimated LAI and the ground truth value is a suitable method. In general, the trend of probability likelihood is slightly steady. And about 77 percent of all validation results lie in the mean value plus or minus one standard variance.
Key words: remote Sensing; inversion; a priori knowledge; spatial spread; uncertainty
发表期数: 2008年5月第1期
引用格式: 屈永华,王锦地. 遥感反演中先验知识空间传播模式及验证[J]. 中国科技论文在线精品论文,2008,1(1):49-56.
 
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