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贝叶斯网络
    The Study on Remote Sensing Data Classification Using Bayesian Network
    遥感数据的贝叶斯网络分类研究
    Study on Hybrid Inversion Scheme under Bayesian Network
    贝叶斯网络支持的地表参数混合反演模式研究
    Data Rectification Based on Bayesian Network
    基于贝叶斯网络的数据校正
    The experimental result indicates that the post classification comparison based on Bayesian Network classification algorithm is a newly effective approach for remote sensing imageries change detection.
    实验结果表明:基于贝叶斯网络分类算法的后分类比较变化检测方法是遥感影像变化检测的一种新的有效方法。
    A PROCESSING METHOD FOR REMOTE SENSING IMAGERY DATA BASED ON BAYESIAN NETWORK MODEL
    基于贝叶斯网络模型的遥感图像数据处理技术
    THE APPLICATION OF THE BAYESIAN NETWORK METHOD TO AIRBORNE DATA CLASSIFICATION
    航空遥感数据的贝叶斯网络分类
    In this paper, we focus on applying modern intelligent tools such as Genetic algorithm and Bayesian network to remote sensing data processing.
    本论文着重于将遗传算法、贝叶斯网络等智能工具和方法应用于遥感数据处理领域。
    Bayesian network is a new pattern classification graph model based on Bayesian statistics, it is an intelligent tool that can integrate prior knowledge and sample information in classification and causality discrimination for data processing.
    贝叶斯网络是在贝叶斯统计基础上发展起来的新的模式分类方法,它提供了一种可以同时利用先验知识和样本信息进行分类和判别因果关系的遥感数据处理工具。
    Thus, it is hot and in the research frontier to apply Genetic algorithm and Bayesian network to the remote sensing data processing.
    将遗传算法、贝叶斯网络等应用于遥感图像数据处理领域已经逐渐成为研究热点。
    Bayesian network is a new inference and express method of uncertain knowledge. It is proposed an inference and express technique for remote sensing imagery data which has complexity and uncertainty based on Bayesian Network Model(BNM).
    贝叶斯网络是一种不确定性知识的推理和描述技术,针对遥感数据的复杂性和不确定性,该文提出了一种基于贝叶斯网络模型的遥感数据推理和描述技术。
    The study results suggest that Bayesian network is likely to become a new practical method for remote sensing data processing.
    研究结果表明,贝叶斯网络可以为遥感数据分类提供一种新方法。
    The remote sensing data Bayesian networks structure training involves the prior knowledge and amount of samples,which is important tache of Bayesian network classification.
    遥感数据的贝叶斯网络结构训练涉及先验知识和样本数量两个方面,是贝叶斯网络结构分类的重要环节。
    This paper depends on the application target and remote sensing bands physical meaning,does the experiment of different combinations of remote sensing bands and amount of samples,the experiment results can provide basic guidelines to the Bayesian network classification.
    该文以应用目标和遥感数据波段的物理意义为先验知识指导,进行了贝叶斯网络结构建立中的遥感数据波段数和样本数的优化组合实验,为贝叶斯网络在遥感数据分类方面提供了基础性实验结果。
    The study results suggest that Bayesian network will be a newly effective approach for remote sensing data change detection.
    实验结果表明,贝叶斯网络为遥感数据的直接变化检测提供了一种新的途径。
    Bayesian network is composed of directed acyclic graph and probability chart; it can modify the prior probability density dynamically and improve the accuracy of classification.
    贝叶斯网络是一个带有概率注释的有向无环图,可以动态地对先验概率密度修正,提高分类精度,也没有严格的数据正态分布前提要求,适合处理不完整复杂的数据。
    The Dynamic Bayesian Network(DBN),which uses the time-series dynamic data to produce credible probabilistic reasoning,is a method developed in 1990s based on the Bayesian network,and offers a way to change analysis from the static viewpoint to the dynamic viewpoint when we carry out remote sensing change detection.
    动态贝叶斯网络是20世纪90年代在贝叶斯网络基础上发展起来的、利用时序动态数据产生可靠概率推理的新方法,动态贝叶斯网络为实现遥感变化检测从静态到动态分析提供了一种新的途径。
 

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