{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:15:30Z","timestamp":1761808530056,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["62101568","ZK21-06"],"award-info":[{"award-number":["62101568","ZK21-06"]}]},{"name":"Scientific Research Program of the National University of Defense Technology (NUDT)","award":["62101568","ZK21-06"],"award-info":[{"award-number":["62101568","ZK21-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 \u00d7 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.<\/jats:p>","DOI":"10.3390\/rs14195060","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:50:01Z","timestamp":1665449401000},"page":"5060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhengbin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}]},{"given":"Anxi","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}]},{"given":"Ben","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}]},{"given":"Zhen","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8343-1209","authenticated-orcid":false,"given":"Xing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","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\u2014ScienceDirect","volume":"59","author":"Kulkarni","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2020.10.019","article-title":"Universal SAR and optical image registration via a novel SIFT framework based on nonlinear diffusion and a polar spatial-frequency descriptor","volume":"171","author":"Yu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","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":"Michael","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3960","DOI":"10.1109\/TGRS.2015.2388495","article-title":"SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture","volume":"53","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1080\/01431161.2018.1448481","article-title":"Multi-sensor remote sensing image change detection based on sorted histograms","volume":"39","author":"Wan","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","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":"Wilfried","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/TGRS.2018.2790483","article-title":"OS-SIFT: A robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas","volume":"56","author":"Xiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, X., Leng, C., Hong, Y., Pei, Z., Cheng, I., and Anup, B. (2021). Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey. Remote Sens., 13.","DOI":"10.3390\/rs13245128"},{"key":"ref_9","unstructured":"Zhao, F., Huang, Q., and Gao, W. (2006, January 14\u201319). Image Matching by Normalized Cross-Correlation. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, W., Su, F., Wang, R., and Fan, J. (2012, January 22\u201327). A visual circle based image registration algorithm for optical and SAR imagery. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351089"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1109\/TGRS.2009.2034842","article-title":"Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas","volume":"48","author":"Suri","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Siddique, M.A., Sarfraz, S.M., Bornemann, D., and Hellwich, O. (2012, January 22\u201327). Automatic registration of SAR and optical images based on mutual information assisted Monte Carlo. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351159"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ijleo.2015.09.199","article-title":"Infrared and visual image registration based on mutual information with a combined particle swarm optimization\u2014Powell search algorithm","volume":"127","author":"Zhuang","year":"2016","journal-title":"Opt. -Int. J. Light Electron Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.cageo.2007.10.005","article-title":"A fast and fully automatic registration approach based on point features for multi-source remote-sensing images","volume":"34","author":"Yu","year":"2008","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shi, X., and Jiang, J. (2016). Automatic registration method for optical remote sensing images with large background variations using line segments. Remote Sens., 8.","DOI":"10.3390\/rs8050426"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/83.366480","article-title":"A contour-based approach to multisensor image registration","volume":"4","author":"Li","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/36.789634","article-title":"A feature-based image registration algorithm using improved chain-code representation combined with invariant moments","volume":"37","author":"Dai","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, S., and Ji, K. (2016, January 10\u201315). A novel method of corner detector for SAR images based on Bilateral Filter. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729706"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TGRS.2015.2420659","article-title":"Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor","volume":"53","author":"Sedaghat","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"157","article-title":"Nonrigid Brain MR Image Registration Using Uniform Spherical Region Descriptor","volume":"21","author":"Liao","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1008045108935","article-title":"Feature detection with automatic scale selection","volume":"30","author":"Lindeberg","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/LGRS.2012.2216500","article-title":"Registration of Optical and SAR Satellite Images by Exploring the Spatial Relationship of the Improved SIFT","volume":"10","author":"Fan","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","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_25","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1080\/01431161.2015.1070321","article-title":"An automatic optical and SAR image registration method with iterative level set segmentation and SIFT","volume":"36","author":"Xu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","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_28","first-page":"77554","article-title":"Remote Sensing Image Registration Based on Phase Congruency Feature Detection and Spatial Constraint Matching","volume":"6","author":"Ma","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5368","DOI":"10.1109\/TGRS.2018.2815523","article-title":"SAR and optical image registration using nonlinear diffusion and phase congruency structural descriptor","volume":"56","author":"Fan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, X., Ai, Y., Zhang, J., and Wang, Z. (2018). A Novel Affine and Contrast Invariant Descriptor for Infrared and Visible Image Registration. Remote Sens., 10.","DOI":"10.3390\/rs10040658"},{"key":"ref_31","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_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Li, S., Lv, X., Ren, J., and Li, J. (2022). A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration. Remote Sens., 14.","DOI":"10.3390\/rs14030630"},{"key":"ref_35","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_36","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_37","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, H., and Huang, Y. (2021). A Rotation-Invariant Optical and SAR Image Registration Algorithm Based on Deep and Gaussian Features. Remote Sens., 13.","DOI":"10.3390\/rs13132628"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1109\/JSTARS.2018.2803212","article-title":"Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching","volume":"11","author":"Merkle","year":"2018","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_39","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. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_40","first-page":"6000705","article-title":"Optical and SAR Image Matching Using Pixelwise Deep Dense Features","volume":"19","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Quan, D., Wang, S., Liang, X., Wang, R., Fang, S., Hou, B., and Jiao, L. (2018, January 22\u201327). Deep generative matching network for optical and SAR image registration. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518653"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isprsjprs.2019.09.010","article-title":"Matching of TerraSAR-X derived ground control points to optical image patches using deep learning","volume":"158","author":"Koppe","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","first-page":"1","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_44","first-page":"1","article-title":"A semi-supervised approach to sar-optical image matching","volume":"4","author":"Hughes","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat.Inf. Sci."},{"key":"ref_45","unstructured":"Jia, H. (2020). Research on Automatic Registration of Optical and SAR Images. [Master\u2019s Thesis, Chang\u2019an University]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1109\/36.673672","article-title":"An optimal multiedge detector for SAR image segmentation","volume":"36","author":"Roger","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3773","DOI":"10.1080\/01431161.2015.1054046","article-title":"An efficient SAR edge detector with a lower false positive rate","volume":"36","author":"Wei","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","first-page":"1390","article-title":"A fast matching method of SAR and optical images using angular weighted orientated gradients","volume":"50","author":"Fan","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ye, Y., Bruzzone, L., Shan, J., and Shen, L. (2017, January 23\u201328). Fast and Robust Structure-based Multimodal Geospatial Image Matching. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128160"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5060\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:49:29Z","timestamp":1760143769000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":49,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14195060"],"URL":"https:\/\/doi.org\/10.3390\/rs14195060","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,10]]}}}