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面向土地利用分类的多源遥感数据混合贝叶斯网络分类器

DOI: 10.6046/gtzyyg.2011.02.09, PP. 47-52

Keywords: 离散化,正态分布检验,混合贝叶斯网络分类器,多源遥感数据,土地利用分类

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Abstract:

传统的离散型贝叶斯网络分类器是将所有变量视为离散变量,或对连续变量做离散化处理。可是离散化不可避免地存在信息损失,且在多源遥感数据的处理和分析中,连续变量的离散化会导致搜索空间的急剧增加和计算及存储量的极大开销。针对这些问题,开发了一种面向土地利用分类的多源遥感数据混合贝叶斯网络分类器,该分类器首先对问题领域的所有变量做正态分布检验,同时将满足高斯分布假设的变量不做离散化而视为连续变量;然后对离散变量和连续变量分别进行参数学习,最后进行参数合并,再用于贝叶斯网络的推理和分类中。通过福州市区土地利用分类的实验表明,该模型优于传统的离散型贝叶斯网络分类器,具有一定的研究和应用价值。

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