{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:52:29Z","timestamp":1776181949976,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023A1515011588"],"award-info":[{"award-number":["2023A1515011588"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62201614"],"award-info":[{"award-number":["62201614"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["6210593"],"award-info":[{"award-number":["6210593"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["23lgpy45"],"award-info":[{"award-number":["23lgpy45"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Shenzhen Science and Technology Program","award":["2023A1515011588"],"award-info":[{"award-number":["2023A1515011588"]}]},{"name":"Shenzhen Science and Technology Program","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Shenzhen Science and Technology Program","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Shenzhen Science and Technology Program","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Shenzhen Science and Technology Program","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Shenzhen Science and Technology Program","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Shenzhen Science and Technology Program","award":["62201614"],"award-info":[{"award-number":["62201614"]}]},{"name":"Shenzhen Science and Technology Program","award":["6210593"],"award-info":[{"award-number":["6210593"]}]},{"name":"Shenzhen Science and Technology Program","award":["23lgpy45"],"award-info":[{"award-number":["23lgpy45"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["2023A1515011588"],"award-info":[{"award-number":["2023A1515011588"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["62201614"],"award-info":[{"award-number":["62201614"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["6210593"],"award-info":[{"award-number":["6210593"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["23lgpy45"],"award-info":[{"award-number":["23lgpy45"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023A1515011588"],"award-info":[{"award-number":["2023A1515011588"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001523"],"award-info":[{"award-number":["62001523"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203465"],"award-info":[{"award-number":["62203465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62201614"],"award-info":[{"award-number":["62201614"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6210593"],"award-info":[{"award-number":["6210593"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["23lgpy45"],"award-info":[{"award-number":["23lgpy45"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["2023A1515011588"],"award-info":[{"award-number":["2023A1515011588"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["62201614"],"award-info":[{"award-number":["62201614"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["6210593"],"award-info":[{"award-number":["6210593"]}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["23lgpy45"],"award-info":[{"award-number":["23lgpy45"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image registration is the basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, the significant nonlinear radiation difference (NRD) and the geometric imaging model difference render the registration quite challenging. To solve this problem, both traditional and deep learning methods are used to extract structural information with dense descriptions of the images, but they ignore that structural information of the image pair is coupled and often process images separately. In this paper, a deep learning-based registration method with a co-attention matching module (CAMM) for SAR and optical images is proposed, which integrates structural feature maps of the image pair to extract keypoints of a single image. First, joint feature detection and description are carried out densely in both images, for which the features are robust to radiation and geometric variation. Then, a CAMM is used to integrate both images\u2019 structural features and generate the final keypoint feature maps so that the extracted keypoints are more distinctive and repeatable, which is beneficial to global registration. Finally, considering the difference in the imaging mechanism between SAR and optical images, this paper proposes a new sampling strategy that selects positive samples from the ground-truth position\u2019s neighborhood and augments negative samples by randomly sampling distractors in the corresponding image, which makes positive samples more accurate and negative samples more abundant. The experimental results show that the proposed method can significantly improve the accuracy of SAR\u2013optical image registration. Compared to the existing conventional and deep learning methods, the proposed method yields a detector with better repeatability and a descriptor with stronger modality-invariant feature representation.<\/jats:p>","DOI":"10.3390\/rs15153879","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T10:25:36Z","timestamp":1691231136000},"page":"3879","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["SAR and Optical Image Registration Based on Deep Learning with Co-Attention Matching Module"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiaxing","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}]},{"given":"Hongtu","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Early Warning Technology, Air Force Early Warning Academy, Wuhan 430019, China"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}]},{"given":"Hejun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}]},{"given":"Guoqian","family":"Wang","sequence":"additional","affiliation":[{"name":"The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/JSTARS.2016.2639580","article-title":"Fast Factorized Backprojection Algorithm for One-Stationary Bistatic Spotlight Circular SAR Image Formation","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.3390\/rs15082215","article-title":"Fast Factorized Backprojection Algorithm in Orthogonal Elliptical Coordinate System for Ocean Scenes Imaging Using Geosynchronous Spaceborne-Airborne VHF UWB Bistatic SAR","volume":"15","author":"Hu","year":"2023","journal-title":"Remote Sens."},{"key":"ref_3","first-page":"3599","article-title":"Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images","volume":"14","author":"Jiang","year":"2023","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"31143","DOI":"10.1109\/ACCESS.2020.2971660","article-title":"High-Efficiency and High-Precision Reconstruction Strategy for P-Band Ultra-Wideband Bistatic Synthetic Aperture Radar Raw Data Including Motion Errors","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.inffus.2020.01.003","article-title":"Pixel Level Fusion Techniques for SAR and Optical Images: A Review","volume":"59","author":"Kulkarni","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.02.006","article-title":"Semantic Segmentation of Slums in Satellite Images Using Transfer Learning on Fully Convolutional Neural Networks","volume":"150","author":"Wurm","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6277","DOI":"10.1109\/TIP.2021.3093766","article-title":"Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images","volume":"30","author":"Sun","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2015.09.005","article-title":"Recent Developments in Large-Scale Tie-Point Matching","volume":"115","author":"Hartmann","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5235913","DOI":"10.1109\/TGRS.2022.3211858","article-title":"Optical and SAR Image Registration Based on Feature Decoupling Network","volume":"60","author":"Xiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1109\/TGRS.2017.2656380","article-title":"Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TGRS.2004.835294","article-title":"On the Possibility of Automatic Multisensor Image Registration","volume":"42","author":"Inglada","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hel-Or, Y., Hel-Or, H., and David, E. (2011, January 6\u201313). Fast Template Matching in Non-Linear Tone-Mapped Images. Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126389"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.012","article-title":"A Review of Multimodal Image Matching: Methods and Applications","volume":"73","author":"Jiang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9059","DOI":"10.1109\/TGRS.2019.2924684","article-title":"Fast and Robust Matching for Multimodal Remote Sensing Image Registration","volume":"57","author":"Ye","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1109\/TIP.2019.2959244","article-title":"RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-Like Algorithm for SAR Images","volume":"53","author":"Dellinger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/LGRS.2016.2600858","article-title":"Remote Sensing Image Registration with Modified SIFT and Enhanced Feature Matching","volume":"14","author":"Ma","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/MGRS.2020.3046356","article-title":"Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1109\/JSTARS.2019.2916560","article-title":"Registration of Multimodal Remote Sensing Image Based on Deep Fully Convolutional Neural Network","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","first-page":"6000705","article-title":"Optical and SAR Image Matching Using Pixelwise Deep Dense Features","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1000513","DOI":"10.1109\/TGRS.2021.3066432","article-title":"MAP-Net: SAR and Optical Image Matching via Image-Based Convolutional Network with Attention Mechanism and Spatial Pyramid Aggregated Pooling","volume":"60","author":"Cui","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.3390\/rs14153599","article-title":"Self-Supervised Keypoint Detection and Cross-Fusion Matching Networks for Multimodal Remote Sensing Image Registration","volume":"14","author":"Li","year":"2022","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wiles, O., Ehrhardt, S., and Zisserman, A. (2021, January 20\u201325). Co-Attention for Conditioned Image Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01566"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yi, K., Trulls, E., Lepetit, V., and Fua, P. (2016, January 8\u201316). LIFT: Learned Invariant Feature Transform. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201323). SuperPoint: Self-Supervised Interest Point Detection and Description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. (2018, January 18\u201323). D2-Net: A Trainable CNN for Joint Description and Detection of Local Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_28","unstructured":"Revaud, J., Weinzaepfel, P., De Souza, C., Pion, N., Csurka, G., Cabon, Y., and Humenberger, M. (2019). R2D2: Repeatable and reliable detector and descriptor. arXiv."},{"key":"ref_29","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Advanced Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Lu, Y., and Sclaroff, S. (2018, January 18\u201323). Local Descriptors Optimized for Average Precision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00069"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L., and Zhu, X. (2018). The Sen1-2 Dataset for Deep Learning in Sar-Optical Data Fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-141-2018"},{"key":"ref_33","unstructured":"Alsallakh, B., Kokhlikyan, N., Miglani, V., Yuan, J., and Reblitz-Richardson, O. (2020). Mind the Pad--CNNs Can Develop Blind Spots. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/B:VISI.0000027790.02288.f2","article-title":"Scale & Affine Invariant Interest Point Detectors","volume":"60","author":"Mikolajczyk","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5622215","DOI":"10.1109\/TGRS.2022.3167644","article-title":"A Multiscale Framework with Unsupervised Learning for Remote Sensing Image Registration","volume":"60","author":"Ye","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3879\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:26:12Z","timestamp":1760127972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3879"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":35,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153879"],"URL":"https:\/\/doi.org\/10.3390\/rs15153879","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,4]]}}}