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高分辨率遥感图像变化检测关键技术研究
其他题名Research on Change Detection of High Resolution Remote Sensing Images
霍春雷
学位类型工学博士
导师周志鑫 ; 卢汉清
2008-12-31
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词高分辨率遥感图像 变化检测 多尺度分析 机器学习 局部特征 Very High Resolution Remote Sensing Images Change Detection Multiscale Analysis Machine Learning Local Features
摘要博士论文对高分辨率遥感图像变化检测关键技术进行了深入研究,主要贡献如下: (1)从成像机理上分析了高分辨率遥感图像变化检测的难点,提出了变化检测新框架。 (2)提出了基于SIFT特征和广义紧互对原型对距离的遥感图像配准新方法,可以得到更多的匹配点对和正确的匹配点对。通过“广义紧互对原型对" 的概念,为不同的特征点匹配方法建立了联系。 (3)提出了混和配准新方法,结合了基于特征的配准方法和基于区域的配准方法的优点,在配准精度、计算效率、自动化程度和鲁棒性等方面可以达到较好的平衡。 (4)提出了基于多尺度融合的对象级变化检测新方法。对象级变化检测可以提高变化类和非变化类的类间可分性,多尺度融合可以减少变化检测对尺度和对象的敏感度。   (5)提出了基于尺度传播的多尺度变化检测方法,将“由粗到精、逐层加细”策略应用到变化检测领域,充分利用了变化特征在不同尺度上的不同统计特性,具有精度高、速度快、对配准误差和视角变化鲁棒性好等优点。 (6)提出了基于渐进直推式SVM的对象级变化检测快速算法。快速多时相分割和基于对象的变化特征分类大大提高了计算效率,渐进直推式SVM使得对象级变化检测方法得以自动进行。 (7)研究并实现了高分辨率遥感图像变化检测原型系统。
其他摘要Some key techniques related to change detection of VHR images are studied in depth in the dissertation. The main contributions of this dissertation include the following issues: (1)The difficulties in change detection of VHR images are analyzed from the viewpoint of the imaging principle, based on which a new change detection framework is presented. (2)A new approach based on SIFT and the distance between the generalized tight pair-wise prototypes is proposed for VHR remote sensing image registration. The proposed method is verified to be effective in increasing the number of matches and the number of correct matches. The relation between different matching methods based on SIFT is established by the concept of the generalized tight pair-wise prototypes. (3)A hybrid approach is proposed for remote sensing image registration. By combining the merits of the feature-based and area-based approaches, the proposed hybrid method can achieve a better balance among accuracy, robustness, efficiency and automation. (4)A novel object-level approach based on multiscale fusion is presented for change detection of VHR images. Object-level change detection is helpful for improving the discriminability between the changed class and the unchanged class, and multiscale fusion is beneficial for mitigating the dependance of the change and the object on the scale. (5)A novel multiscale change detection approach is proposed based on scale propagation. By taking advantages of coarse-to-fine strategy, the different statistical distributions of change features at different scales are captured. The proposed approach is high in accuracy, efficient in computation, robust to mis-registration and view-angle variation. (6) A fast object-level approach is presented for change detection of VHR images based on the progressive transductive SVM. The computation efficiency is improved significantly by taking advantages of fast multi-temporal segmentation and object-specific change feature classification, and object-level change detection is implemented automatically by utilizing the progressive transductive SVM. (7)To confirm the effectiveness of the approaches proposed in this dissertation, a prototype system is developed for change detection of VHR images.
馆藏号XWLW1333
其他标识符200518014628093
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6136
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
霍春雷. 高分辨率遥感图像变化检测关键技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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