Progressive Symmetric Registration for Multimodal Remote Sensing Imagery

IF 7.5 1区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-12-09 DOI:10.1109/TGRS.2024.3514305
Heng Yan;Ailong Ma;Yanfei Zhong
{"title":"Progressive Symmetric Registration for Multimodal Remote Sensing Imagery","authors":"Heng Yan;Ailong Ma;Yanfei Zhong","doi":"10.1109/TGRS.2024.3514305","DOIUrl":null,"url":null,"abstract":"Image registration forms the foundation of collaborative processing in multimodal remote sensing imagery (MRSI). However, high-resolution MRSIs frequently display complex distortions due to imaging characteristics and terrain variations, with both global and local distortions present. Effectively addressing these complex distortions necessitates the identification of uniformly and densely distributed corresponding points across the entire image. Existing methods primarily focus on global affine distortions and often extract only sparse and unevenly distributed corresponding points, which makes the effective handling of these coexisting distortions a significant challenge. To address this problem, we propose a progressive symmetric registration learning network (PSRNet) for MRSIs. In PSRNet, multimodal remote sensing image registration (MRSIR) is redefined as a symmetric dense regression task, differing from the traditional pipeline that concentrates on unidirectional sparse transformation parameter prediction. Specifically, PSRNet consists of three primary components: 1) a multiscale feature projector (MFP), which employs a dual-branch structure with nonshared weights to achieve modality-specific representation of different modal images across multiple scales, 2) a progressive cross-modal transformer (PCMT) to further mine modality-invariant features and progressively predict symmetric deformation fields, and 3) a symmetric consistency loss (SCL) function capable of elegantly achieving high-precision reversible alignment of image pairs, encompassing endpoint error loss, bidirectional alignment loss, and smoothness loss. Experimental results demonstrate that PSRNet achieves more comprehensive and advanced registration performance on our self-constructed large-scale high-resolution MRSIR dataset, which includes complex global-local geometric distortions and significant nonlinear radiometric differences (NRD).","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-17"},"PeriodicalIF":7.5000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Geoscience and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10787065/","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Image registration forms the foundation of collaborative processing in multimodal remote sensing imagery (MRSI). However, high-resolution MRSIs frequently display complex distortions due to imaging characteristics and terrain variations, with both global and local distortions present. Effectively addressing these complex distortions necessitates the identification of uniformly and densely distributed corresponding points across the entire image. Existing methods primarily focus on global affine distortions and often extract only sparse and unevenly distributed corresponding points, which makes the effective handling of these coexisting distortions a significant challenge. To address this problem, we propose a progressive symmetric registration learning network (PSRNet) for MRSIs. In PSRNet, multimodal remote sensing image registration (MRSIR) is redefined as a symmetric dense regression task, differing from the traditional pipeline that concentrates on unidirectional sparse transformation parameter prediction. Specifically, PSRNet consists of three primary components: 1) a multiscale feature projector (MFP), which employs a dual-branch structure with nonshared weights to achieve modality-specific representation of different modal images across multiple scales, 2) a progressive cross-modal transformer (PCMT) to further mine modality-invariant features and progressively predict symmetric deformation fields, and 3) a symmetric consistency loss (SCL) function capable of elegantly achieving high-precision reversible alignment of image pairs, encompassing endpoint error loss, bidirectional alignment loss, and smoothness loss. Experimental results demonstrate that PSRNet achieves more comprehensive and advanced registration performance on our self-constructed large-scale high-resolution MRSIR dataset, which includes complex global-local geometric distortions and significant nonlinear radiometric differences (NRD).
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
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing 工程技术-地球化学与地球物理
CiteScore
11.50
自引率
28.00%
发文量
1912
审稿时长
4.0 months
期刊介绍: IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
期刊最新文献
Automatic Extraction of Roads From Multisource Geospatial Data Using Fusion Attention Network and Regularization Algorithm Optimizing Satellite Image Analysis: Leveraging Variational Autoencoders Latent Representations for Direct Integration Multi-scale Feature Knowledge Distillation and Implicit Object Discovery for Few-shot Object Detection in Remote Sensing Images Contour-enhanced Visual State-Space Model for Remote Sensing Image Classification SPTS: Single-Pixel in Time-Series Triangle Model For Estimating Surface Soil Moisture
×
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