基于多尺度相位一致性的多模态遥感图像配准

Song Cui, Yanfei Zhong
{"title":"基于多尺度相位一致性的多模态遥感图像配准","authors":"Song Cui, Yanfei Zhong","doi":"10.1109/PRRS.2018.8486287","DOIUrl":null,"url":null,"abstract":"Automatic matching of multi-modal remote sensing images remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. This paper introduces the phase congruency model with illumination and contrast invariance for image matching, and extends the model to a novel image registration method, named as multi-scale phase consistency (MS-PC). The Euclidean distance between MS-PC descriptors is used as similarity metric to achieve correspondences. The proposed method is evaluated with four pairs of multi-model remote sensing images. The experimental results show that MS-PC is more robust to the radiation differences between images, and performs better than two popular method (i.e. SIFT and SAR-SIFT) in both registration accuracy and tie points number.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Multi-Modal Remote Sensing Image Registration Based on Multi-Scale Phase Congruency\",\"authors\":\"Song Cui, Yanfei Zhong\",\"doi\":\"10.1109/PRRS.2018.8486287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic matching of multi-modal remote sensing images remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. This paper introduces the phase congruency model with illumination and contrast invariance for image matching, and extends the model to a novel image registration method, named as multi-scale phase consistency (MS-PC). The Euclidean distance between MS-PC descriptors is used as similarity metric to achieve correspondences. The proposed method is evaluated with four pairs of multi-model remote sensing images. The experimental results show that MS-PC is more robust to the radiation differences between images, and performs better than two popular method (i.e. SIFT and SAR-SIFT) in both registration accuracy and tie points number.\",\"PeriodicalId\":197319,\"journal\":{\"name\":\"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PRRS.2018.8486287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRRS.2018.8486287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

由于多模态遥感图像之间存在显著的非线性辐射差异,多模态遥感图像的自动匹配一直是遥感图像分析中的一个难点。本文引入了具有光照和对比度不变性的相位一致性模型用于图像匹配,并将该模型扩展为一种新的图像配准方法,称为多尺度相位一致性(MS-PC)。采用MS-PC描述符之间的欧几里得距离作为相似性度量来实现对应。利用四对多模型遥感图像对该方法进行了评价。实验结果表明,MS-PC对图像之间的辐射差异具有更强的鲁棒性,在配准精度和结合点数量上都优于常用的两种方法(SIFT和SAR-SIFT)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Modal Remote Sensing Image Registration Based on Multi-Scale Phase Congruency
Automatic matching of multi-modal remote sensing images remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. This paper introduces the phase congruency model with illumination and contrast invariance for image matching, and extends the model to a novel image registration method, named as multi-scale phase consistency (MS-PC). The Euclidean distance between MS-PC descriptors is used as similarity metric to achieve correspondences. The proposed method is evaluated with four pairs of multi-model remote sensing images. The experimental results show that MS-PC is more robust to the radiation differences between images, and performs better than two popular method (i.e. SIFT and SAR-SIFT) in both registration accuracy and tie points number.
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