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Alternative TitleStudy on Change Detection of High Resolution Remote Sensing Images
Thesis Advisor刘成林 ; 潘春洪
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机技术
Keyword高分辨率遥感图像 变化检测 稀疏表示 字典学习 High-resolution Remote Sensing Images Change Detection Sparse Representation Dictionary Learning
Abstract遥感图像变化检测作为对地观测的一个重要技术具有广泛的需求,如军事目标监视、城市规划、环境监控、自然灾害监测等。高分辨率图像相对于中低分辨率图像可以提供更多的细节信息,有利于得到更加详细的变化检测结果。因此,近年来高分辨率遥感图像变化检测受到了更加广泛的关注。然而,由于高分辨率遥感图像的复杂性,现有高分辨率图像变化检测技术发展相对滞后,大部分方法在检测精度、速度和通用性方面还存在很多不足。为此,迫切需要对其进行理论和算法方面的研究。 本文首先分析了高分辨率遥感图像变化检测的几个难点,并对现有变化检测方法进行了系统的总结。在综合考虑了稀疏表示与高分辨率遥感图像二者的特点之后,本文将稀疏表示用于高分辨率图像变化检测中并提出了变化检测的新方法。本文的主要工作和贡献如下: 提出了一种基于判别字典学习的高分辨率遥感图像变化检测方法。该方法将特征提取和分类器训练两个过程结合。这种耦合的学习方式既有利于学习到判别的特征又能得到好的分类器。在QuickBird高分辨率遥感图像上的实验结果验证了这一方法的有效性。 提出了一种基于稀疏表示的分层聚类方法用于高分辨率遥感图像变化检测。该方法通过学习分层字典建模变化特征的多峰分布模式,同时使用稀疏表示误差度量样本到类别的距离。相对于传统基于聚类的变化检测方法,本文的方法可以有效处理变化特征的多峰分布情况。
Other AbstractChange detection in remote sensing images finds extensive applications, such as military target surveillance, urban planning, environmental monitoring and natural disaster detection. Compared to low- and median-resolution remote sensing images, high-resolution remote sensing images contain more details about the earth surface and enable detecting more subtle land use and land-cover changes. However, due to the complexity of high-resolution remote sensing images, the change detection techniques in the literature are immature, and mostly insufficient in terms of accuracy, speed and versatility. In consequence, it is very urgent to develop methods and algorithms for high-resolution remote sensing image change detection. Based on a comprehensive survey of the state of the art of high-resolution image change detection, this thesis proposes two methods taking into account the characteristics of high-resolution remote sensing images and the merits of sparse representation. Specifically, A new method based on discriminative dictionary learning is proposed for high-resolution image change detection. In this method, the feature representation and the classifier are learned simultaneously, so as to improve the discrimination capability. Experimental results on the QuickBird image datasets confirm the effectiveness of the proposed method. A hierarchical clustering method based on sparse representation is proposed for high resolution image change detection. It learns hierarchical dictionary to model the multimodal distribution of change features, and the sampleto- class is measured by the sparse representation error. Compared to the traditional clustering-based change detection methods, the proposed approach is novel in dealing with the multimodal distribution.
Other Identifier2011E8014661093
Document Type学位论文
Recommended Citation
GB/T 7714
丁昆. 高分辨率遥感图像变化检测研究[D]. 中国科学院自动化研究所. 中国科学院大学,2014.
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