{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:21:38Z","timestamp":1760232098990,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"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":["42101359","2020C003R"],"award-info":[{"award-number":["42101359","2020C003R"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"High-level Talents Innovation and Entrepreneurship Project of Quanzhou City","award":["42101359","2020C003R"],"award-info":[{"award-number":["42101359","2020C003R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic Aperture Radar technology is highly convenient for monitoring the glacier surface motion in unfavorable areas due to its advantages of being independent of time and weather conditions. A novel glacier motion monitoring method based on the deep matching network (DMN) is proposed in this paper. The network learns the relationship between the glacier SAR image patch-pairs and the corresponding matching labels in an end-to-end manner. Unlike conventional methods that utilize shallow feature tracking, the DMN performs a similarity measurement of deep features, which comprises feature extraction and a metric network. Feature extraction adopts the framework of a Siamese neural network to improve the training efficiency and dense connection blocks to increase the feature utilization. In addition, a self-sample learning method is introduced to generate training samples with matching labels. The experiments are performed on simulated SAR images and real SAR intensity images of the Taku Glacier and the Yanong Glacier, respectively. The results confirm the superiority of the DMN presented in the paper over other methods, even in case of strong noise. Furthermore, a quantitative 2D velocity field of real glaciers is obtained to provide reliable support for high-precision, long-term and large-scale automatic glacier motion monitoring.<\/jats:p>","DOI":"10.3390\/rs14205128","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"5128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Glacier Motion Monitoring Using a Novel Deep Matching Network with SAR Intensity Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-2484","authenticated-orcid":false,"given":"Huifang","family":"Shen","sequence":"first","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"given":"Shudong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-4083","authenticated-orcid":false,"given":"Li","family":"Fang","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2373-8799","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450052, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1126\/science.abh4455","article-title":"A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya","volume":"373","author":"Shugar","year":"2021","journal-title":"Science"},{"key":"ref_2","first-page":"927","article-title":"Remote Sensing of Glaciers: Techniques for Topographic, Spatial and Thematic Mapping of Glaciers","volume":"56","author":"Rees","year":"2009","journal-title":"J. Glaciol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2013.04.010","article-title":"Glacier surface velocity estimation using repeat TerraSAR-X images: Wavelet- vs. correlation-based image matching","volume":"82","author":"Schubert","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3628","DOI":"10.1016\/j.rse.2008.05.015","article-title":"Motion patterns of Nabesna Glacier (Alaska) revealed by interferometric SAR techniques","volume":"112","author":"Li","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1007\/s00190-019-01325-y","article-title":"Three-dimensional deformation time series of glacier motion from multiple-aperture DlnSAR observation","volume":"93","author":"Samsonov","year":"2019","journal-title":"J. Geodesy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1029\/2006GL026883","article-title":"Measuring two-dimensional movements using a single InSAR pair","volume":"33","author":"Bechor","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TGRS.2002.805079","article-title":"Glacier Motion Estimation Using SAR Offset-Tracking Producers","volume":"40","author":"Strozzi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s00034-009-9130-7","article-title":"Fast Normalized Cross-Correlation","volume":"28","author":"Yoo","year":"2009","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3189\/S0022143000003075","article-title":"Flow of Glaciar Moreno, Argentina, from repeat-pass Shuttle Imaging Radar images: Comparison of the phase correlation method with radar interferometry","volume":"45","author":"Michel","year":"1999","journal-title":"J. Glaciol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.isprsjprs.2016.08.012","article-title":"Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images","volume":"121","author":"Fang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Muhuri, A., Natsuaki, R., Bhattacharya, A., and Hirose, A. (2015, January 1\u20134). Glacier surface velocity estimation using stokes vector correlation. Proceedings of the IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore.","DOI":"10.1109\/APSAR.2015.7306281"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1126\/science.234.4780.1105","article-title":"Antarctica: Measuring Glacier Velocity from Satellite Images","volume":"234","author":"Lucchitta","year":"1986","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Korosov, A.A., and Rampal, P. (2017). A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9030258"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent Scatterers in SAR Interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1080\/07038992.2001.10854936","article-title":"Velocities and Flux of the Filchner Ice Shelf and its Tributaries Determined from Speckle Tracking Interferometry","volume":"27","author":"Gray","year":"2001","journal-title":"Can. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"225","DOI":"10.5589\/m05-010","article-title":"Glacier dynamics in the Canadian High Arctic from RADARSAT-1 speckle tracking","volume":"31","author":"Short","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/LGRS.2013.2256104","article-title":"Measuring Coseismic Displacements with Point-Like Targets Offset Tracking","volume":"11","author":"Hu","year":"2013","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_18","first-page":"51","article-title":"Mutual information as a similarity measure for remote sensing image registration","volume":"4383","author":"Johnson","year":"2001","journal-title":"Proc. SPIE\u2014Int. Soc. Opt. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1109\/TGRS.2003.817664","article-title":"Performance of mutual information similarity measure for registration of multitemporal remote sensing images","volume":"41","author":"Chen","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/TGRS.2002.802501","article-title":"An automated parallel image registration technique based on the correlation of wavelet features","volume":"40","author":"Moigne","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TMI.2015.2455416","article-title":"Image Registration Based on Autocorrelation of Local Structure","volume":"35","author":"Li","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1080\/01431160902927622","article-title":"Applicability of the SIFT operator to geometric SAR image registration","volume":"31","author":"Schwind","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","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."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108316","DOI":"10.1016\/j.patcog.2021.108316","article-title":"Explainable Scale Distillation for Hyperspectral Image Classification","volume":"122","author":"Shi","year":"2022","journal-title":"Pattern Recogn."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, C., Dang, Y., Fang, L., Lv, Z., and Shen, H. (2021). Attention-Guided Multispectral and Panchromatic Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13234823"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2015, January 7\u201312). Learning to Compare Image Patches via Convolutional Neural Networks. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"ref_30","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., and Berg, A. (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 (CVPR), Boston, MA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Charrier, L., Godet, P., Rambour, C., Weissgerber, F., and Koeniguer, E.C. (2022, January 21\u201325). Analysis of dense coregistration methods applied to optical and SAR time-series for ice flow estimations. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266643"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Laurens, V.e. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","unstructured":"Hadsell, R., Chopra, S., and Lecun, Y. (2006, January 17\u201322). Dimensionality Reduction by Learning an Invariant Mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TGRS.2013.2238947","article-title":"Adaptive Multilooking of Airborne Single-Pass Multi-Baseline InSAR Stacks","volume":"52","author":"Schmitt","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ye, Z., Xu, Y., Chen, H., Zhu, J., Tong, X., and Stilla, U. (2020). Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study. Remote Sens., 12.","DOI":"10.3390\/rs12040696"},{"key":"ref_38","first-page":"491","article-title":"Image GeoRectification and Feature Tracking toolbox: ImGRAFT","volume":"4","author":"Messerli","year":"2014","journal-title":"Geosci. Instrum. Methods Data Syst. Discuss."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fitch, A.J., Kadyrov, A., Christmas, W.J., and Kittler, J. (2002, January 2\u20135). Orientation correlation. Proceedings of the Proceedings of the British Machine Vision Conference 2002, BMVC 2002, Cardiff, UK.","DOI":"10.5244\/C.16.11"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TGRS.2006.888937","article-title":"Automatic and Precise Orthorectification, Coregistration, and Subpixel Correlation of Satellite Images, Application to Ground Deformation Measurements","volume":"45","author":"Leprince","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nagashima, S., Aoki, T., Higuchi, T., and Kobayashi, K. (2006, January 12\u201315). A Subpixel Image Matching Technique Using Phase-Only Correlation. Proceedings of the IEEE International Symposium on Intelligent Signal Processing and Communications, Yonago, Japan.","DOI":"10.1109\/ISPACS.2006.364751"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1364\/OL.33.000156","article-title":"Efficient subpixel image registration algorithms","volume":"33","author":"Thurman","year":"2008","journal-title":"Opt. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:53:42Z","timestamp":1760144022000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":42,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205128"],"URL":"https:\/\/doi.org\/10.3390\/rs14205128","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,13]]}}}