利用动态贝叶斯网络进行多时相遥感变化检测

欧阳赟; 马建文; 戴芹

doi:10.3724/SP.J.1146.2005.01023

利用动态贝叶斯网络进行多时相遥感变化检测

doi: 10.3724/SP.J.1146.2005.01023

欧阳赟^{①②;马建文},
马建文,
戴芹

基金项目:

国家863计划(2006AA12Z130)资助课题

计量
- 文章访问数: 3279
- HTML全文浏览量: 79
- PDF下载量: 1092
- 被引次数: 0
出版历程
- 收稿日期: 2005-08-18
- 修回日期: 2006-01-11
- 刊出日期: 2007-03-19

Multi-temporal Remote Sensing Change Detection Using Dynamic Bayesian Networks

Ouyang Yun^{①②;马建文},
Ma Jian-wen,
Dai Qin

摘要

摘要: 利用动态贝叶斯网络(DBNs)在处理不同时相遥感数据时可以一次性输入多个时间段的数据，同时完成分类和建立输出类别之间的关联。采用北京东部地区1994年、2001年和2003年5月份Landsat TM遥感数据进行实验，实验结果表明：基于DBNs的变化检测方法是遥感变化检测的一种新的有效方法，在遥感时序数据动态变化分析的研究方面也展示了巨大的发展潜力。
- 贝叶斯网络;动态贝叶斯网络;遥感变化检测
Abstract: Utilizing Dynamic Bayesian Networks (DBNs) to deal with multi-temporal remote sensing data, the multi-temporal data of different time can be input simultaneously, and the classification and the acquirement of relationships between the output types can be finished simultaneously. Using the Landsat TM remote sensing data of Beijing eastern area acquired in May of 1994, 2001 and 2003 for the experiment, the experimental results indicate that the DBN-based change detection method is a new effective method of remote sensing change detection, and show its great potential for the research on the analysis of the dynamic changes of remote sensing time-series data.

HTML全文

参考文献(1)

[1] Pearl J. Probabilistic Reasoning in Intelligent Systems. San Francisco, CA: Morgan Kaufmann. 1988, Chapter 3. [2] Heckerman D. A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, USA, 1995: 57. [3] 史忠植等. 知识发现. 北京: 清华大学出版社, 2002: 169-198. Murphy K P. Dynamic Bayesian Networks: Representation, inference and learning. PhD Thesis, UC Berkeley, 2002. [4] Pavlovic V, Rehg J M, Cham T J, and Murphy K P. A dynamic Bayesian network approach to figure tracking using learned dynamic models. Proceedings of IEEE International Conference on Computer Vision 1999, Corfu, Greece, 1999: 94-101. [5] Nefian A, Liang L, Pi X, Liu X, and Murphy K P. Dynamic Bayesian networks for audio-visual speech recognition. Journal of Applied Signal Processing, 2002, 11: 1-15. [6] Kwon J and Murphy K P. Modeling freeway traffic using coupled HMMs. Technical report, Department of Computer Science, UC Berkeley, 2000. [7] Zou M and Conzen S D. A new dynamic Bayesian network approach for identifying gene regulatory networks from time course microarray data[J].Bioinformatics.2005, 21(1):71-79 [8] Liang Bob. 未来处理器架构进行应用驱动的研究报告. Intel Microprocessor Research Forum. Beijing, China, 2002. [9] Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition[J].Proc. IEEE.1989, 77(2):257-286 [10] Bengio Y. Markovian models for sequential data. Neural Computing Surveys, 1999, 2: 129-162. [11] Roweis S and Ghahramani Z. A unifying review of linear Gaussian models[J].Neural Computation.1999, 11(2):305-345 [12] Minka T. From hidden Markov models to linear dynamical systems. Technical report, MIT, 1999. [13] 赵英时等. 遥感应用分析原理与方法. 北京: 科学出版社. 2003, 第7章. [14] 陈雪, 戴芹, 马建文，李小文. 贝叶斯网络分类算法在遥感数据变化检测上的应用. 北京师范大学学报(自然科学版), 2005, 41(1): 97-100. Chen Xue, Dai Qin, and Ma Jian-wen, et al. Application of Bayesian network classification to remote sensing change detection. Journal of Beijing Normal University (Natural Science), 2005, 41(1): 97-100. [15] 戴芹, 马建文, 欧阳赟. 利用贝叶斯网络进行遥感变化检测. 中国图像图形学报, 2005, 10(6): 705-709. [16] Dawid A P. Applications of a general propagation algorithm for probabilistic expert systems[J].Statistics and Computing.1992, 2:25-36 [17] Nilsson D. An efficient algorithm for finding the most probable configurations in probabilistic expert systems[J].Statistics and Computing.1998, 8:159-173 [18] Jensen F V. An introduction to Bayesian networks. London: UCL Press, 1996: 1-178. [19] Jensen F V, Olesen K G, and Andersen S K. An algebra of Bayesian belief universes for knowledge-based system[J].Networks.1990, 20:637-659

施引文献

资源附件(0)

访问统计