{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:28:36Z","timestamp":1766068116534,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively.<\/jats:p>","DOI":"10.3390\/rs16224291","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T06:06:54Z","timestamp":1731996414000},"page":"4291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features"],"prefix":"10.3390","volume":"16","author":[{"given":"Jie","family":"Xu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Qifeng","family":"Lai","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Dongyan","family":"Wei","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xinchun","family":"Ji","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Ge","family":"Shen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Hong","family":"Yuan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4979","DOI":"10.1109\/TVT.2020.2981093","article-title":"Extending shadow matching to tightly-coupled GNSS\/INS integration system","volume":"69","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1007\/s00190-021-01578-6","article-title":"Improving PPP\u2013RTK in urban environment by tightly coupled integration of GNSS and INS","volume":"95","author":"Li","year":"2021","journal-title":"J. Geod."},{"key":"ref_3","first-page":"6","article-title":"INS Aided GNSS Pseudo-range Error Prediction Using Machine Learning for Urban Vehicle Navigation","volume":"24","author":"Zhang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18224","DOI":"10.1109\/TITS.2022.3167710","article-title":"3D LiDAR aided GNSS NLOS mitigation in urban canyons","volume":"23","author":"Wen","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4098","DOI":"10.1109\/TVT.2021.3069212","article-title":"3D point clouds data super resolution-aided LiDAR odometry for vehicular positioning in urban canyons","volume":"70","author":"Yue","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., and Song, S. (2018, January 21\u201325). Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8461224"},{"key":"ref_7","unstructured":"Su, Y., Chunlin, H., and Wentai, L. (2006). Ground Penetrating Radar: Theory and Applications, Science Press."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Solla, M., P\u00e9rez-Gracia, V., and Fontul, S. (2021). A review of GPR application on transport infrastructures: Troubleshooting and best practices. Remote Sens., 13.","DOI":"10.3390\/rs13040672"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TGRS.2020.2984951","article-title":"Landmine detection using autoencoders on multipolarization GPR volumetric data","volume":"59","author":"Bestagini","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ijmst.2020.12.009","article-title":"Measurement of overburden failure zones in close-multiple coal seams mining","volume":"31","author":"Li","year":"2021","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1002\/rob.21605","article-title":"Localizing ground penetrating radar: A step toward robust autonomous ground vehicle localization","volume":"33","author":"Cornick","year":"2016","journal-title":"J. Field Robot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1109\/LRA.2020.2976310","article-title":"Autonomous navigation in inclement weather based on a localizing ground penetrating radar","volume":"5","author":"Ort","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Baikovitz, A., Sodhi, P., Dille, M., and Kaess, M. (2021, January 27). Ground encoding: Learned factor graph-based models for localizing ground penetrating radar. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636764"},{"key":"ref_14","first-page":"2657","article-title":"Ground penetrating radar image template matching based on symmetrical structure features","volume":"37","author":"Zhang","year":"2022","journal-title":"Prog. Geophys."},{"key":"ref_15","first-page":"1265","article-title":"A deep learning assisted ground penetrating radar localization method","volume":"44","author":"Ni","year":"2022","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bi, B., Shen, L., Zhang, P., Huang, X., Xin, Q., and Jin, T. (2023). TSVR-Net: An End-to-End Ground-Penetrating Radar Images Registration and Location Network. Remote Sens., 15.","DOI":"10.3390\/rs15133428"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kim, G., and Kim, A. (2018, January 1\u20135). Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593953"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/34.993558","article-title":"Shape matching and object recognition using shape contexts","volume":"24","author":"Belongie","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/10095020.2023.2264337","article-title":"Classification of urban interchange patterns using a model combining shape context descriptor and graph convolutional neural network","volume":"27","author":"Yang","year":"2024","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1109\/JSEN.2021.3138846","article-title":"Multi-stage refinement feature matching using adaptive ORB features for robotic vision navigation","volume":"22","author":"Sun","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_21","first-page":"1","article-title":"Combination of SIFT and Canny edge detection for registration between SAR and optical images","volume":"19","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","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 Computer Vision\u2014ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria. Proceedings, Part I. 9.","DOI":"10.1007\/11744023_32"},{"key":"ref_23","unstructured":"Liu, C., Cao, H., and Fan, Y. (2022). Study on Adaptive DCP Image Optimization Algorithm Combined with OSTU Threshold Method. Fire Control. Command. Control., 047."},{"key":"ref_24","first-page":"9","article-title":"Panoramic Camera Image Mosaic Method Based on Feature Points","volume":"50","author":"Xu","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_25","unstructured":"(2017). Specifications for Design of Highway Asphalt Pavement (Standard No. JTG D50\u20142017 (EN))."},{"key":"ref_26","unstructured":"(2016). Code for Design of Railway Earth Structure (Standard No. TB 10001-2016)."},{"key":"ref_27","unstructured":"Deng, P. (2015). Research on Detection of Railway Tunnel Seepage with Train-Mounted GPR. [Master\u2019s Thesis, Southwest Jiaotong University]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:34:16Z","timestamp":1760114056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":27,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224291"],"URL":"https:\/\/doi.org\/10.3390\/rs16224291","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,11,18]]}}}