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遥感技术与应用  2007, Vol. 22 Issue (4): 524-530    DOI: 10.11873/j.issn.1004-0323.2007.4.524
图像处理     
基于SIFT特征和广义紧互对原型对距离的遥感图像配准方法
霍春雷1,周志鑫1,2,刘青山1,卢汉清1
(1.中国科学院自动化研究所模式识别国家重点实验室,北京 100080;2.北京市遥感信息研究所,北京 100854)
Remote Sensing Image Registration Based on SIFT and the Distance Between Generalized Tight Pair-wise Prototypes
HUO Chun-lei1, ZHOU Zhi-xin2,1, LIU Qing-shan1, LU Han-qing1
(1.National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing, 100080,China; 2.Beijing Institute of Remote Sensing,Beijing100854,China)
 全文: PDF 
摘要:

主要讨论SIFT(Scale Invariant Feature Transform)及其在遥感图像配准中的应用。首先介绍了基于特征点的遥感图像配准的一般框架;针对基于特征点的遥感图像配准中的两个基本问题—鲁棒的特征点提取和特征点匹配,提出了基于SIFT特征点和广义紧互对原型对距离的遥感图像配准新方法,并通过“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。与已有的相关工作相比,该方法可以得到更多的匹配点对和正确的匹配点对。数值试验证明了该方法的有效性和鲁棒性。

关键词: 遥感图像配准SIFT广义紧互对原型对特征点匹配    
Abstract:

SIFT (Scale Invariant Feature Transform) and its application on remote sensing image registration is discussed in this paper. First, the keypoint-based framework of remote sensing image registration is described; After analyzing robust feature extractor and keypoints matching which are two
key problems in the image registration, a new approach based on SIFT and the distance between generalized tight pair-wise prototypes for remote sensing image registration is proposed; The relation between different matching methods based on SIFT is established by the concept of generalized tight pairwise prototypes. Compared with the related work, our method can get more number of correct point matches and total number of point matches. The experimental results demonstrate the robustness and efficiency of the algorithm.

Key words: Remote sensing image registration    SIFT    Tight pair-wise prototypes    Keypoint matching
收稿日期: 2006-11-30 出版日期: 2011-11-25
:  TN 911.73  
基金资助:

863计划、优秀创新群体项目基金(编号60121302)资助。

作者简介: 霍春雷(1977-),男,博士研究生,研究方向为图像处理、模式识别。
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引用本文:

霍春雷,周志鑫,刘青山,卢汉清. 基于SIFT特征和广义紧互对原型对距离的遥感图像配准方法[J]. 遥感技术与应用, 2007, 22(4): 524-530.

链接本文:

http://www.rsta.ac.cn/CN/Y2007/V22/I4/524

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