TOPS model image registration study in topographic undulating areas

Wenting Liu, L. Han, Jie Yu
{"title":"TOPS model image registration study in topographic undulating areas","authors":"Wenting Liu, L. Han, Jie Yu","doi":"10.1109/ICGMRS55602.2022.9849381","DOIUrl":null,"url":null,"abstract":"The characteristics of sentinel data using terrain observation by progressive scans (TOPS) determine that its registration process is more complex and requires higher accuracy, and the synthetic aperture radar (SAR) images obtained from areas with large terrain undulations have obvious shadows and overlay masks. In this paper, we investigate the difficult problem of SAR image registration in TOPS imaging mode in areas with large terrain undulations and design a multi-stage registration method based on geometric registration, incoherent cross correlation (ICC) method, and enhanced spectral diversity (ESD) to complete the registration of sentinel image pairs and compare the accuracy of the traditional cross-correlation method with that of this paper. The experiments prove that the method described in this paper has the advantage that the registration accuracy does not depend on the image coherence, and it can still maintain a high accuracy even in areas with large topographic relief.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGMRS55602.2022.9849381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The characteristics of sentinel data using terrain observation by progressive scans (TOPS) determine that its registration process is more complex and requires higher accuracy, and the synthetic aperture radar (SAR) images obtained from areas with large terrain undulations have obvious shadows and overlay masks. In this paper, we investigate the difficult problem of SAR image registration in TOPS imaging mode in areas with large terrain undulations and design a multi-stage registration method based on geometric registration, incoherent cross correlation (ICC) method, and enhanced spectral diversity (ESD) to complete the registration of sentinel image pairs and compare the accuracy of the traditional cross-correlation method with that of this paper. The experiments prove that the method described in this paper has the advantage that the registration accuracy does not depend on the image coherence, and it can still maintain a high accuracy even in areas with large topographic relief.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
地形起伏区TOPS模型图像配准研究
逐级扫描地形观测(TOPS)哨兵数据的特点决定了其配准过程较为复杂,精度要求较高,地形起伏较大区域的合成孔径雷达(SAR)图像存在明显的阴影和叠加掩模。本文针对地形起伏较大地区TOPS成像模式下SAR图像配准的难点问题,设计了一种基于几何配准、非相干互相关(ICC)和增强光谱分集(ESD)的多阶段配准方法,完成了前哨图像对的配准,并与传统互相关方法的配准精度进行了比较。实验证明,本文方法的优点是配准精度不依赖于图像的相干性,即使在地形起伏较大的区域也能保持较高的配准精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Research on UAV remote sensing multispectral image compression based on CNN MDNet: A Multi-modal Dual Branch Road Extraction Network Using Infrared Information Quantitative Evaluation of Digital Orthophoto Map Influence of shallow ocean front on propagation characteristics of low frequency sound energy flow Application of GA-BP neural network in prediction of chl-a concentration in Wuliangsu Lake
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1