基于SURF和互信息的混合SAR图像配准算法

Bo Pang, Hong Sun, Qiuze Yu, Peng Wu
{"title":"基于SURF和互信息的混合SAR图像配准算法","authors":"Bo Pang, Hong Sun, Qiuze Yu, Peng Wu","doi":"10.1109/APSAR.2015.7306229","DOIUrl":null,"url":null,"abstract":"Aim at the problems of features points is difficult to extract, image deformation is difficult to estimate and low registration accuracy in synthetic aperture radar (SAR) image. This paper present a hybrid SAR image registration algorithm base on speeded up robust features (SURF) and mutual information. The hybrid registration algorithm consists of coarse registration and fine registration, respectively. In the coarse registration stage, use SURF algorithm finds the regional extreme value feature points, due to the SURF algorithm ignore structural features, we use Harris corner detection algorithm to extract corner feature, thus extend the feature points to improve the accuracy of coarse registration. Acquire image transform parameters by affine transformation model. In the fine registration stage, through maximize mutual information (MI) to complete the further registration, acquire the more accuracy of image transform parameters, and achieve the high accuracy SAR image registration. The experimental results shows that the method presented in this paper can improve the accuracy of the algorithm.","PeriodicalId":350698,"journal":{"name":"2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A hybrid SAR image registration algorithm base on SURF and mutual information\",\"authors\":\"Bo Pang, Hong Sun, Qiuze Yu, Peng Wu\",\"doi\":\"10.1109/APSAR.2015.7306229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim at the problems of features points is difficult to extract, image deformation is difficult to estimate and low registration accuracy in synthetic aperture radar (SAR) image. This paper present a hybrid SAR image registration algorithm base on speeded up robust features (SURF) and mutual information. The hybrid registration algorithm consists of coarse registration and fine registration, respectively. In the coarse registration stage, use SURF algorithm finds the regional extreme value feature points, due to the SURF algorithm ignore structural features, we use Harris corner detection algorithm to extract corner feature, thus extend the feature points to improve the accuracy of coarse registration. Acquire image transform parameters by affine transformation model. In the fine registration stage, through maximize mutual information (MI) to complete the further registration, acquire the more accuracy of image transform parameters, and achieve the high accuracy SAR image registration. The experimental results shows that the method presented in this paper can improve the accuracy of the algorithm.\",\"PeriodicalId\":350698,\"journal\":{\"name\":\"2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APSAR.2015.7306229\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSAR.2015.7306229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

针对合成孔径雷达(SAR)图像存在特征点难以提取、图像变形难以估计、配准精度低等问题。提出了一种基于加速鲁棒特征和互信息的混合SAR图像配准算法。混合配准算法分为粗配准和精配准。在粗配准阶段,使用SURF算法寻找区域极值特征点,由于SURF算法忽略了结构特征,我们使用Harris角点检测算法提取角点特征,从而扩展特征点,提高粗配准的精度。利用仿射变换模型获取图像变换参数。在精细配准阶段,通过最大互信息(MI)完成进一步配准,获得更精确的图像变换参数,实现SAR图像的高精度配准。实验结果表明,本文提出的方法可以提高算法的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid SAR image registration algorithm base on SURF and mutual information
Aim at the problems of features points is difficult to extract, image deformation is difficult to estimate and low registration accuracy in synthetic aperture radar (SAR) image. This paper present a hybrid SAR image registration algorithm base on speeded up robust features (SURF) and mutual information. The hybrid registration algorithm consists of coarse registration and fine registration, respectively. In the coarse registration stage, use SURF algorithm finds the regional extreme value feature points, due to the SURF algorithm ignore structural features, we use Harris corner detection algorithm to extract corner feature, thus extend the feature points to improve the accuracy of coarse registration. Acquire image transform parameters by affine transformation model. In the fine registration stage, through maximize mutual information (MI) to complete the further registration, acquire the more accuracy of image transform parameters, and achieve the high accuracy SAR image registration. The experimental results shows that the method presented in this paper can improve the accuracy of the algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信