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贝叶斯网络分类算法在遥感数据变化检测上的应用 被引量：8

APPLICATION OF BAYESIAN NETWORK CLASSIFICATION TO REMOTE SENSING CHANGE DETECTION
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摘要 以北京通州地区1996年5月29日和2001年5月19日2个时期的遥感影像为例介绍了基于贝叶斯网络 (BN)的分类算法,在此基础上实现了2个不同实现遥感影像的变化检测,实验结果表明:基于BN分类的后分类比较变 化检测方法是遥感数据变化检测的一种新的有效方法. Taking the Landsat TM data acquired on 1996-05-29 and 2001-05-19 in Beijing as an example, the Bayesian Network classification algorithm is introduced in detail and then the change detection using the temporal remote sensing data is realized. The result indicates that the post classification comparison based on Bayesian Network classification algorithm is a newly effective approach for remote sensing change detection.
作者 陈雪 戴芹 马建文 李小文
机构地区 北京师范大学地理与遥感学院 中国科学院遥感应用研究所
出处 《北京师范大学学报（自然科学版）》 CAS CSCD 北大核心 2005年第1期97-100,共4页 Journal of Beijing Normal University(Natural Science)
基金 国家自然科学基金资助项目(40371086)
关键词 贝叶斯网络 变化检测 分类后比较 Bayesian network change detection post classification comparison
分类号 P237 [天文地球—摄影测量与遥感]
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参考文献7

  • 1慕春棣,tsinghua.edu.cn,戴剑彬,叶俊.用于数据挖掘的贝叶斯网络[J].软件学报,2000,11(5):660-666. 被引量：97
  • 2胡玉胜,涂序彦,崔晓瑜,程乾生.基于贝叶斯网络的不确定性知识的推理方法[J].计算机集成制造系统-CIMS,2001,7(12):65-68. 被引量：70
  • 3周颜军,王双成,王辉.基于贝叶斯网络的分类器研究[J].东北师大学报（自然科学版）,2003,35(2):21-27. 被引量：54
  • 4Ashbindu Singh. Digital change detection techniques using remotely-sensed data[J]. Int J Remote Sensing,1989,10:989.
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二级参考文献20

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  • 101999-03-15

共引文献208

同被引文献88

引证文献8

二级引证文献87

北京师范大学学报（自然科学版）
2005年 第1期

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