{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T23:37:42Z","timestamp":1784590662864,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61471079"],"award-info":[{"award-number":["61471079"]}],"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":["61976032"],"award-info":[{"award-number":["61976032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments.<\/jats:p>","DOI":"10.3390\/rs14112527","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T08:41:33Z","timestamp":1653468093000},"page":"2527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Improved L2Net for Repetitive Texture Image Registration with Intensity Difference Heterogeneous SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3718-4726","authenticated-orcid":false,"given":"Peng","family":"Men","sequence":"first","affiliation":[{"name":"Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5025-1922","authenticated-orcid":false,"given":"Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jubai","family":"An","sequence":"additional","affiliation":[{"name":"Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanyu","family":"Li","sequence":"additional","affiliation":[{"name":"Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2016.07.004","article-title":"Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization","volume":"120","author":"Tuia","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0924-2716(03)00016-9","article-title":"Satellite multi-sensor data analysis of urban surface temperatures and landcover","volume":"58","author":"Dousset","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2006.01.021","article-title":"Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR\/InSAR, ETM+, Quickbird) synergy","volume":"102","author":"Hyde","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake damage assessment of buildings using VHR optical and SAR imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2005.02.002","article-title":"Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards","volume":"59","author":"Tralli","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1175\/JCLI-D-12-00068.1","article-title":"An initial assessment of Antarctic sea ice extent in the CMIP5 models","volume":"26","author":"Turner","year":"2013","journal-title":"J. Clim."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6265","DOI":"10.1175\/JCLI-D-16-0455.1","article-title":"Sea ice trends in climate models only accurate in runs with biased global warming","volume":"30","author":"Rosenblum","year":"2017","journal-title":"J. Clim."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1002\/grl.50578","article-title":"Can natural variability explain observed Antarctic sea ice trends? New modeling evidence from CMIP5","volume":"40","author":"Polvani","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision (IEEE), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.5194\/tc-11-1835-2017","article-title":"Open-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature tracking and pattern matching","volume":"11","author":"Muckenhuber","year":"2017","journal-title":"Cryosphere"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5174","DOI":"10.1109\/TGRS.2017.2703084","article-title":"Sea ice drift tracking from sequential SAR images using accelerated-KAZE features","volume":"55","author":"Demchev","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"357","DOI":"10.5194\/tc-9-357-2015","article-title":"Comparing C-and L-band SAR images for sea ice motion estimation","volume":"9","author":"Lehtiranta","year":"2015","journal-title":"Cryosphere"},{"key":"ref_14","unstructured":"Dierking, W. (2013). Sea Ice Classification on Different Spatial Scales for Operational and Scientific Use, European Space Agency (ESA)."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/el:20082477","article-title":"Multi-spectral remote image registration based on SIFT","volume":"44","author":"Yi","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/LGRS.2008.2011751","article-title":"Robust scale-invariant feature matching for remote sensing image registration","volume":"6","author":"Li","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, W., Sim, D., and Oh, S.J. (2021). A CNN-based high-accuracy registration for remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13081482"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/JSTARS.2020.3042887","article-title":"Improving land cover segmentation across satellites using domain adaptation","volume":"14","author":"Bengana","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3816","DOI":"10.1109\/TGRS.2020.3020804","article-title":"Generative adversarial network-based full-space domain adaptation for land cover classification from multiple-source remote sensing images","volume":"59","author":"Ji","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mohajerani, S., and Saeedi, P. (April, January 28). Cloud-Net: An end-to-end cloud detection algorithm for Landsat 8 imagery. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium (IEEE 2019), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898776"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6587","DOI":"10.1109\/TGRS.2016.2587321","article-title":"Multimodal remote sensing image registration with accuracy estimation at local and global scales","volume":"54","author":"Uss","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2019.03.002","article-title":"Robust registration for remote sensing images by combining and localizing feature-and area-based methods","volume":"151","author":"Feng","year":"2019","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1109\/TNNLS.2018.2888757","article-title":"A novel neural network for remote sensing image matching","volume":"30","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/LGRS.2017.2781741","article-title":"Remote sensing image registration using convolutional neural network features","volume":"15","author":"Ye","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1142\/S0218001493000339","article-title":"Signature verification using a \u201csiamese\u201d time delay neural network","volume":"7","author":"Bromley","year":"1993","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, H., Chen, M., Chen, T., and Li, D. (2018). Matching of remote sensing images with complex background variations via Siamese convolutional neural network. Remote Sens., 10.","DOI":"10.3390\/rs10020355"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/LGRS.2018.2799232","article-title":"Identifying corresponding patches in SAR and optical images with a pseudo-siamese CNN","volume":"15","author":"Hughes","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., and Li, S. (2018, January 19\u201321). Weighted res-unet for high-quality retina vessel segmentation. Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), IEEE 2018, Hangzhou, China.","DOI":"10.1109\/ITME.2018.00080"},{"key":"ref_32","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., and Berg, A.C. (2015, January 7\u201312). Matchnet: Unifying feature and metric learning for patch-based matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Cui, Y., Belongie, S., and Hays, J. (2015, January 7\u201312). Learning deep representations for ground-to-aerial geolocalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299135"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., and Moreno-Noguer, F. (2015, January 7\u201313). Discriminative learning of deep convolutional feature point descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.22"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Melekhov, I., Kannala, J., and Rahtu, E. (2016, January 20\u201324). Image patch matching using convolutional descriptors with euclidean distance. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan.","DOI":"10.1007\/978-3-319-54526-4_46"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tian, Y., Fan, B., and Wu, F. (2017, January 21\u201326). L2Net: Deep learning of discriminative patch descriptor in euclidean space. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.649"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TGRS.2012.2236845","article-title":"Sea ice motion tracking from sequential dual-polarization RADARSAT-2 images","volume":"52","author":"Komarov","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_39","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":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, H., Wang, P., and Jing, L. (2021). An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13173425"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:18:26Z","timestamp":1760138306000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112527"],"URL":"https:\/\/doi.org\/10.3390\/rs14112527","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}