| 基于高斯过程的高分辨率遥感图像变化检测 |
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| 引用本文： | 陈克明,周志鑫,卢汉清,胡文龙,孙显.基于高斯过程的高分辨率遥感图像变化检测[J].遥感学报,2012,16(6):1192-1204. |
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| 作者姓名： | 陈克明 周志鑫 卢汉清 胡文龙 孙显 |
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| 作者单位： | 中国科学院空间信息处理与应用系统技术重点实验室 北京 100190;中国科学院电子学研究所 北京 100190;北京遥感信息研究所 北京 100192;北京遥感信息研究所 北京 100192;中国科学院自动化研究所模式识别国家重点实验室 北京 100190;中国科学院空间信息处理与应用系统技术重点实验室 北京 100190;中国科学院电子学研究所 北京 100190;中国科学院空间信息处理与应用系统技术重点实验室 北京 100190;中国科学院电子学研究所 北京 100190 |
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| 基金项目： | 国家自然科学基金项目(编号: 41001285) |
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| 摘 要： | 本文首先通过理论分析,探讨了高斯过程分类器在高分辨率遥感图像变化检测应用中的优势与不足,并针对高斯过程分类器的不足给出了相应的解决方法;其次,提出了一种基于空间上下文相关的高斯过程变化检测方法;最后,通过多个高分辨率遥感实验数据集上的实验设计与分析,验证了高斯过程分类器在高分辨率遥感图像变化检测中的应用能力,并证明了本文提出的解决方法的有效性.
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| 关 键 词： | 高斯过程 变化检测 高分辨率 支持向量机 马尔可夫随机场模型 |
| 收稿时间： | 2011/11/14 0:00:00 |
| 修稿时间： | 2012/2/27 0:00:00 |
Gaussian process approach to change detection for high resolution remote sensing image |
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| CHEN Keming,ZHOU Zhixin,LU Hanqing,HU Wenlong and SUN Xian.Gaussian process approach to change detection for high resolution remote sensing image[J].Journal of Remote Sensing,2012,16(6):1192-1204. |
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| Authors: | CHEN Keming ZHOU Zhixin LU Hanqing HU Wenlong and SUN Xian |
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| Institution: | Key Laboratory of Geospatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China;Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;Beijing Institute of Remote Sensing, Beijing 100192, China;Beijing Institute of Remote Sensing, Beijing 100192, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Geospatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China;Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Geospatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China;Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | Gaussian process (GP) represents a powerful theoretical framework for Bayesian classif ication. Despite GP classifier have gained prominence in recent years, it remains an approach whose potentialities are not yet suff iciently known in remote sensing community. This paper gives a thorough investigation of GP CLASSIFIER for high resolution (HR) multi-temporal image change detection. Firstly, we give a detailed analysis of the capabilities of GP classif ier in theory. Secondly, we elaborately explore the advantages and disadvantages of the GP classif iers. Finally, we design several experiments to test the performance of the GP classif ier for HR remote sensing image change detection. Moreover, we propose a novel approach for improving the capacities of GP classif ier in remote sensing image change detection. The proposed context-sensitive change detection method is achieved by analyzing the posterior probability of probabilistic GP classif ier within a markov random f ield (MRF) framework. In particular, the method consists of two steps: (1) A supervised initialization is founded on a probabilistic GP classif ier; (2) A MRF regularization aims at ref ining the posterior probability by employing the spatial context information. Five experiments carried out on HR remote sensing image set validate the power of GP classif ier for change detection and also the effectiveness of our proposed methods. |
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| Keywords: | Gaussian process (GP) change detection high resolution (HR) support vector machine (SVM) markov random field (MRF) |
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