A Bayesian Network-Based Method to Alleviate the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy Water Content Retrieval
A Bayesian Network-Based Method to Alleviate the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy Water Content Retrieval
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基于贝叶斯网络的缓解不适定逆问题的方法：叶面积指数和冠层水分反演案例研究
DOI：
10.1109/tgrs.2015.2442999
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发表时间：
2015-06
影响因子：
8.2
通讯作者：
Xing Li
中科院分区：
文献类型：
--
作者：
Xingwen Quan;Binbin He;Xing Li
利用辐射传输模型从遥感数据中反演植被参数时，通常会遇到反演问题的不适定，从而大大降低了反演参数的精度。本研究的目的是使用贝叶斯网络为基础的方法，使不适定的逆问题的缓解。这是通过将无模型参数之间的相关性引入其先验联合概率分布（PJPD）来实现的，从而降低了不切实际的组合的概率。三种抽样策略旨在设计三种类型的PJPD考虑不同的相关性（由相关矩阵表示）。它们分别是由独立自由参数组成的多元均匀分布、基于简单相关矩阵的多元均匀分布和基于复杂相关矩阵的多元高斯分布。应用PROSAIL_5B（前景-5 + 4SAIL）模式反演了Landsat 8产品的叶面积指数（LAI）和冠层含水量（CWC）。结果表明，该方法大大提高了目标参数的精度水平，叶面积指数的决定系数R2分别为0.69、0.77和0.82，均方根误差RMSE分别为0.55、0.51和0.44 m2 · m-2，R2分别为0.68、0.78和0.84，RMSE分别为230、198、199、1 m-2，CWC为166g· m-2。因此，该方法可以有效地缓解反演问题的不适定性，在植被参数反演中具有广泛的应用前景。
Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases the precision level of retrieved parameters. The purpose of this study was to use a Bayesian network-based method to allow the alleviation of the ill-posed inverse problem. This was achieved by introducing the correlations between the model free parameters into their prior joint probability distribution (PJPD), allowing the reduction of the probabilities of unrealistic combinations. Three sampling strategies intended to design three types of PJPDs that considered different correlations (represented by a correlation matrix) were presented. They were multivariate uniform distribution composed by independent free parameters, multivariate uniform distribution based on a simple correlation matrix, and multivariate Gaussian distribution based on a complicated correlation matrix, respectively. A case study of the presented method to retrieve leaf area index (LAI) and canopy water content (CWC) using the PROSAIL_5B (PROSPECT-5 + 4SAIL) model from Landsat 8 products was implemented. Results indicate that the presented method greatly improves the precision level of target parameters, with the coefficient of determination R2 of 0.69, 0.77, and 0.82 and root-mean-square error (RMSE) of 0.55, 0.51, and 0.44 m2 · m-2 for LAI and R2 = 0.68, 0.78, and 0.84 and RMSE = 230, 198, and 166 g · m-2 for CWC, respectively. Hence, the ill-posed inverse problem can be alleviated by the presented method, which can be widely applied for vegetation parameters retrieval.
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用于检索地表植被特征的贝叶斯网络算法
影响因子：
13.5
作者：
Wan, Huawei;Zhou, Guoqing;Qu, Yonghua;Wang, Jindi;Li, Xiaowen
通讯作者：
Li, Xiaowen
DOI：
10.1016/0034-4257(85)90072-0
发表时间：
1984-02
期刊：
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影响因子：
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影响因子：
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作者：
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