{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T08:59:44Z","timestamp":1768467584557,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:00:00Z","timestamp":1619136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["SQ2020YFA070264"],"award-info":[{"award-number":["SQ2020YFA070264"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Science and Technology Projects of Jilin Province","award":["20200503002SF"],"award-info":[{"award-number":["20200503002SF"]}]},{"DOI":"10.13039\/501100013072","name":"Major Science and Technology Project of Hainan Province","doi-asserted-by":"publisher","award":["ZDKJ2019007"],"award-info":[{"award-number":["ZDKJ2019007"]}],"id":[{"id":"10.13039\/501100013072","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.<\/jats:p>","DOI":"10.3390\/rs13091657","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T02:12:57Z","timestamp":1619316777000},"page":"1657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7830-3704","authenticated-orcid":false,"given":"Junyan","family":"Lu","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Company Ltd., Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongguang","family":"Jia","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Company Ltd., Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tie","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Electro-Mechanical Engineering Institute, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Company Ltd., Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Company Ltd., Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruifei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Company Ltd., Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/S0262-8856(03)00137-9","article-title":"Image registration methods: A survey","volume":"21","author":"Zitova","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1145\/146370.146374","article-title":"A survey of image registration techniques","volume":"24","author":"Brown","year":"1992","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Le Moigne, J. (2017, January 23\u201328). Introduction to remote sensing image registration. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127519"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4834","DOI":"10.1109\/TGRS.2019.2893310","article-title":"A novel two-step registration method for remote sensing images based on deep and local features","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). Surf: Speeded up robust features. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_8","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 IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_9","first-page":"1281","article-title":"Fast explicit diffusion for accelerated features in nonlinear scale spaces","volume":"34","author":"Alcantarilla","year":"2011","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1137\/080732730","article-title":"ASIFT: A new framework for fully affine invariant image comparison","volume":"2","author":"Morel","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_12","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","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":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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":"171","DOI":"10.1109\/LGRS.2011.2163491","article-title":"Multilevel SIFT matching for large-size VHR image registration","volume":"9","author":"Huo","year":"2011","journal-title":"IEEE Geosci. Rem. Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.1109\/TGRS.2010.2042813","article-title":"Fully automatic subpixel image registration of multiangle CHRIS\/Proba data","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.1109\/TGRS.2011.2144607","article-title":"Uniform robust scale-invariant feature matching for optical remote sensing images","volume":"49","author":"Sedaghat","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1109\/TGRS.2011.2109389","article-title":"Automatic image registration through image segmentation and SIFT","volume":"49","author":"Goncalves","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TGRS.2013.2281391","article-title":"A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information","volume":"52","author":"Gong","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2014.01.009","article-title":"A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences","volume":"90","author":"Ye","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/LGRS.2014.2343471","article-title":"An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images","volume":"12","author":"Kupfer","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2014.2325970","article-title":"A novel point-matching algorithm based on fast sample consensus for image registration","volume":"12","author":"Wu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprs-annals-III-1-9-2016","article-title":"Hopc: A novel similarity metric based on geometric structural properties for multi-modal remote sensing image matching","volume":"3","author":"Ye","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2017.12.012","article-title":"A deep learning framework for remote sensing image registration","volume":"145","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_28","unstructured":"Marcos, D., Tuia, D., Kellenberger, B., Zhang, L., Bai, M., Liao, R., and Urtasun, R. (2018, January 18\u201322). Learning deep structured active contours end-to-end. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cheng, D., Liao, R., Fidler, S., and Urtasun, R. (2019, January 16\u201320). Darnet: Deep active ray network for building segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00761"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Sengupta, D., and Terzopoulos, D. (2020, January 23\u201328). End-to-end trainable deep active contour models for automated image segmentation: Delineating buildings in aerial imagery. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_43"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hamaguchi, R., Fujita, A., Nemoto, K., Imaizumi, T., and Hikosaka, S. (2018, January 12\u201315). Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, CA, USA.","DOI":"10.1109\/WACV.2018.00162"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6699","DOI":"10.1109\/TGRS.2018.2841808","article-title":"Vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Feng, Y., Diao, W., Zhang, Y., Li, H., Chang, Z., Yan, M., Sun, X., and Gao, X. (August, January 28). Ship Instance segmentation from remote sensing images using sequence local context module. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897948"},{"key":"ref_34","first-page":"974","article-title":"Reconstruction of digital surface model of single-view remote sensing image by semantic segmentation network","volume":"43","author":"Lu","year":"2021","journal-title":"J. Electr. Inf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1628005","DOI":"10.3788\/AOS202040.1628005","article-title":"Airport detection method combined with continuous learning of residual-based network on remote sensing image","volume":"40","author":"Li","year":"2020","journal-title":"Acta Opt. Sin."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1528003","DOI":"10.3788\/AOS202040.1528003","article-title":"Domestic multispectral image classification based on multilayer perception convolutional neural network","volume":"40","author":"Zhu","year":"2020","journal-title":"Acta Opt. Sin."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"205902","DOI":"10.1109\/ACCESS.2020.3037350","article-title":"SAR: Single-stage anchor-free rotating object detection","volume":"8","author":"Lu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., and Li, L. (2020, January 23\u201328). Solo: Segmenting objects by locations. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"ref_39","first-page":"1","article-title":"SOLOv2: Dynamic and fast instance segmentation","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xie, J., Chen, X., and Wang, J. (2020, January 23\u201328). Segfix: Model-agnostic boundary refinement for segmentation. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_29"},{"key":"ref_41","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019, April 09). High-Resolution Representations for Labeling Pixels and Regions. Available online: https:\/\/arxiv.org\/abs\/1904.04514."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 14\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","article-title":"Visual pattern recognition by moment invariants","volume":"8","author":"Hu","year":"1962","journal-title":"IRE Trans. Inf. Theory"},{"key":"ref_45","unstructured":"Huang, Z., and Leng, J. (2010, January 16\u201318). Analysis of Hu\u2019s moment invariants on image scaling and rotation. Proceedings of the IEEE 2010 2nd International Conference on Computer Engineering and Technology, Chengdu, China."},{"key":"ref_46","first-page":"73","article-title":"The DGPF-test on digital airborne camera evaluation overview and test design","volume":"2","author":"Cramer","year":"2010","journal-title":"PFG Photogramm. Fernerkund. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Silberman, N., Sontag, D., and Fergus, R. (2014, January 6\u201312). Instance segmentation of indoor scenes using a coverage loss. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_40"},{"key":"ref_48","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., and Sorkine-Hornung, A. (July, January 26). A benchmark dataset and evaluation methodology for video object segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_49","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019, June 17). MMDetection: Open Mmlab Detection toolbox and Benchmark. Available online: https:\/\/arxiv.org\/abs\/1906.07155."},{"key":"ref_50","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., and Girshick, R. (2020, January 14\u201319). Pointrend: Image segmentation as rendering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00982"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:52:09Z","timestamp":1760161929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,23]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091657"],"URL":"https:\/\/doi.org\/10.3390\/rs13091657","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,23]]}}}