{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:21:10Z","timestamp":1766067670051,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"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":["U22A20568","42071444","2022YFB3904101"],"award-info":[{"award-number":["U22A20568","42071444","2022YFB3904101"]}],"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":["U22A20568","42071444","2022YFB3904101"],"award-info":[{"award-number":["U22A20568","42071444","2022YFB3904101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Technologies Research and Development","award":["U22A20568","42071444","2022YFB3904101"],"award-info":[{"award-number":["U22A20568","42071444","2022YFB3904101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multimodal images refer to images obtained by different sensors, and there are serious nonlinear radiation differences (NRDs) between multimodal images for photos of the same object. Traditional multimodal image matching methods cannot achieve satisfactory results in most cases. In order to better solve the NRD in multimodal image matching, as well as the rotation and scale problems, we propose a two-step matching method based on co-occurrence scale space combined with the second-order Gaussian steerable filter (G-CoFTM). We first use the second-order Gaussian steerable filter and co-occurrence filter to construct the image\u2019s scale space to preserve the image\u2019s edge and detail features. Secondly, we use the second-order gradient direction to calculate the images\u2019 principal direction, and describe the images\u2019 feature points through improved GLOH descriptors. Finally, after obtaining the rough matching results, the optimized 3DPC descriptors are used for template matching to complete the fine matching of the images. We validate our proposed G-CoFTM method on eight different types of multimodal datasets and compare it with five state-of-the-art methods: PSO-SIFT, CoFSM, RIFT, HAPCG, and LPSO. Experimental results show that our proposed method has obvious advantages in matching success rate (SR) and the number of correct matches (NCM). On eight different types of datasets, compared with CoFSM, RIFT, HAPCG, and LPSO, the mean SRs of G-CoFSM are 17.5%, 6.187%, 30.462%, and 32.21%, respectively, and the mean NCMs are 5.322, 11.503, 8.607, and 16.429 times those of the above four methods.<\/jats:p>","DOI":"10.3390\/rs14235976","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"5976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Two-Step Matching Method Based on Co-Occurrence Scale Space Combined with Second-Order Gaussian Steerable Filter"],"prefix":"10.3390","volume":"14","author":[{"given":"Genyi","family":"Wan","sequence":"first","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Ruofei","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Chaohong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Yusheng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7178-9817","authenticated-orcid":false,"given":"Zhen","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Ke","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jmir.2018.06.001","article-title":"Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration","volume":"50","author":"Alam","year":"2019","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0031-9155\/46\/3\/201","article-title":"Medical image registration","volume":"46","author":"Hill","year":"2001","journal-title":"Phys. Med. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yogheedha, K., Nasir, A., Jaafar, H., and Mamduh, S. (2018, January 15\u201317). Automatic vehicle license plate recognition system based on image processing and template matching approach. Proceedings of the 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), Kuching, Malaysia.","DOI":"10.1109\/ICASSDA.2018.8477639"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Markiewicz, J., Abratkiewicz, K., Gromek, A., Samczynski, W., and Gromek, D. (2019). Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems. Sensors, 19.","DOI":"10.3390\/s19245500"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hou, B., Wang, J., and Zhou, H. (2019, January 22\u201324). Navigation landmark recognition and matching algorithm based on the improved SURF. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8996908"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.isprsjprs.2020.02.005","article-title":"LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data","volume":"162","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"De Alban, J.D.T., Connette, G.M., Oswald, P., and Webb, E.L. (2018). Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes. Remote Sens., 10.","DOI":"10.3390\/rs10020306"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/LGRS.2018.2868704","article-title":"A Conditional Adversarial Network for Change Detection in Heterogeneous Images","volume":"16","author":"Niu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TIP.2019.2933747","article-title":"Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise Based Markov Random Field Model","volume":"29","author":"Touati","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/42.563664","article-title":"Multimodality image registration by maximization of mutual information","volume":"16","author":"Maes","year":"1997","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and van Gool, L. (2006). SURF: Speeded Up Robust Features. Computer Vision\u2013ECCV 2006, Springer.","DOI":"10.1007\/11744023_32"},{"key":"ref_13","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/A:1007958904918","article-title":"Alignment by maximization of mutual information","volume":"24","author":"Viola","year":"1997","journal-title":"Int. J. Comput. Vision"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ye, Y., and Shen, L. (2016, January 12\u201319). HOPC: A novel similarity metric based on geometric structural properties for multi-modal remote sensing image matching. Proceedings of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprsannals-III-1-9-2016"},{"key":"ref_16","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_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":"3296","DOI":"10.1109\/TIP.2019.2959244","article-title":"RIFT: Multimodal image matching based on radiation-variation insensitive feature transform","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., and Fua, P. (2016). LIFT: Learned invariant feature transform. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. (2019, January 16\u201320). D2-net: A trainable cnn for joint description and detection of local features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_21","unstructured":"Efe, U., Ince, K.G., and Alatan, A. (2019, January 19\u201325). Dfm: A performance baseline for deep feature matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual Event."},{"key":"ref_22","first-page":"1","article-title":"NCFT: Automatic Matching of Multimodal Image Based on Nonlinear Consistent Feature Transform","volume":"19","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jevnisek, R., and Shai, A. (2017, January 21\u201326). Co-occurrence Filter. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.406"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2584","DOI":"10.1109\/TIP.2022.3157450","article-title":"Multi-Modal Remote Sensing Image Matching Considering Co-Occurrence Filter","volume":"31","author":"Yao","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","unstructured":"Jiang, Y. (2013, January 14\u201316). Optical\/SAR image registration based on cross-correlation with multi-scale and multi-direction Gabor characteristic matrixes. Proceedings of the 2013 IET International Radar Conference (IRC), Xi\u2019an, China."},{"key":"ref_26","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":"Flusser","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6587","DOI":"10.1109\/TGRS.2016.2587321","article-title":"Multimodal remote sensing images registration with accuracy estimation at local and global scales","volume":"54","author":"Uss","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"6451","DOI":"10.1109\/TGRS.2020.2976865","article-title":"OS-PC: Combining feature representation and 3-D phase correlation for subpixel optical and SAR image registration","volume":"58","author":"Xiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","first-page":"506","article-title":"PCA-SIFT: A more distinctive representation for local image descriptors","volume":"4","author":"Ke","year":"2004","journal-title":"CVPR"},{"key":"ref_33","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_34","first-page":"12440","article-title":"Self-similarity features for multimodal remote sensing image matching","volume":"14","author":"Xiong","year":"2021","journal-title":"IEEE J.-STARS."},{"key":"ref_35","first-page":"1727","article-title":"Heterologous Images Matching Considering Anisotropic Weighted Moment and Absolute Phase Orientation","volume":"46","author":"Yao","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7811109","DOI":"10.1109\/JPHOT.2022.3144227","article-title":"LPSO: Multi-source image matching considering the description of local phase sharpness orientation","volume":"14","author":"Yang","year":"2022","journal-title":"IEEE Photonics J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hughes, L., Schmitt, M., and Zhu, X. (2018). Mining hard negative samples for SAR-optical image matching using generative adversarial networks. Remote Sens., 10.","DOI":"10.3390\/rs10101552"},{"key":"ref_38","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_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":"Han","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","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_41","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_42","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_43","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/757582976","article-title":"Scale-space theory: A basic tool for analyzing structures at different scales","volume":"21","author":"Lindeberg","year":"1994","journal-title":"J. Appl. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.isprsjprs.2022.04.011","article-title":"A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients","volume":"188","author":"Ye","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/S0031-3203(98)00091-0","article-title":"An overlap invariant entropy measure of 3D medical image alignment","volume":"32","author":"Studholme","year":"1999","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2021.09.010","article-title":"Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features","volume":"181","author":"Zhu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1123\/jab.15.3.318","article-title":"Estimate of the optimum cutoff frequency for the Butterworth low-pass digital filter","volume":"15","author":"Yu","year":"1999","journal-title":"J. Appl. Biomech."},{"key":"ref_49","first-page":"1","article-title":"Image features from phase congruency","volume":"1","author":"Kovesi","year":"1999","journal-title":"Videre J. Comput. Vis. Res."},{"key":"ref_50","unstructured":"Kuglin, C.D. (1975, January 26\u201328). The phase correlation image alignment method. Proceedings of the International Conference on Cybernetics and Society\/IEEE Systems, Man, and Cybernetics Society, New York, NY, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wan, G., Wang, Y., Wang, T., Zhu, N., Zhang, R., and Zhong, R. (2022). Automatic Registration for Panoramic Images and Mobile LiDAR Data Based on Phase Hybrid Geometry Index Features. Remote Sens., 14.","DOI":"10.3390\/rs14194783"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:56Z","timestamp":1760146016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":51,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14235976"],"URL":"https:\/\/doi.org\/10.3390\/rs14235976","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,25]]}}}