{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T20:08:40Z","timestamp":1769458120911,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"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":["42174026"],"award-info":[{"award-number":["42174026"]}],"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":["2021YFE011004"],"award-info":[{"award-number":["2021YFE011004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42174026"],"award-info":[{"award-number":["42174026"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFE011004"],"award-info":[{"award-number":["2021YFE011004"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province.<\/jats:p>","DOI":"10.3390\/rs14205067","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T06:13:27Z","timestamp":1665468807000},"page":"5067","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Deep Learning Application for Deformation Prediction from Ground-Based InSAR"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianfeng","family":"Han","sequence":"first","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2093-4295","authenticated-orcid":false,"given":"Honglei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Youfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Zhaowei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Runcheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Beijing Institute of Geological Hazard Prevention, Beijing 100120, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"ref_1","unstructured":"Lingua, A.M., Piatti, D., and Rinaudo, F. (2008, January 25). Remote monitoring of a landslide using an integration of GB-INSAR and LIDAR techniques. Proceedings of the 21st Congress of the International Society for Photogrammetry and Remote Sensing, Beijing, China."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2014.02.003","article-title":"MELISSA, a new class of ground based InSAR system. An example of application in support to the Costa Concordia emergency","volume":"91","author":"Broussolle","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.geomorph.2015.04.018","article-title":"Sinkhole monitoring and early warning: An experimental and successful GB-InSAR application","volume":"241","author":"Emanuele","year":"2015","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Calvari, S., Di Traglia, F., Ganci, G., Giudicepietro, F., Macedonio, G., Cappello, A., Nolesini, T., Pecora, E., Bilotta, G., and Centorrino, V. (2020). Overflows and Pyroclastic Density Currents in March-April 2020 at Stromboli Volcano Detected by Remote Sensing and Seismic Monitoring Data. Remote Sens., 12.","DOI":"10.3390\/rs12183010"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s10346-015-0563-8","article-title":"An integrated methodology for landslides\u2019 early warning systems","volume":"13","author":"Barla","year":"2016","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1186\/s40623-018-0951-0","article-title":"Monitoring ground deformation of eruption center by ground-based interferometric synthetic aperture radar (GB-InSAR): A case study during the 2015 phreatic eruption of Hakone Volcano","volume":"70","author":"Kuraoka","year":"2018","journal-title":"Earth Planets Space"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.geomorph.2017.10.023","article-title":"Tracking morphological changes and slope instability using spaceborne and ground-based SAR data","volume":"300","author":"Di","year":"2018","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.geomorph.2019.03.014","article-title":"Combination of GNSS, satellite InSAR, and GB-InSAR remote sensing monitoring to improve the understanding of a large landslide in high alpine environment","volume":"335","author":"Carla","year":"2019","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106345","DOI":"10.1016\/j.enggeo.2021.106345","article-title":"Monitoring and analysis of the exceptional displacements affecting debris at the top of a highly disaggregated rockslide","volume":"294","author":"Carla","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10346-020-01500-9","article-title":"Investigating the kinematics of the unstable slope of Barber\u00e0 de la Conca (Catalonia, Spain) and the effects on the exposed facilities by GBSAR and multi-source conventional monitoring","volume":"18","author":"Dario","year":"2021","journal-title":"Landslides"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Long, S., Tong, A., Yuan, Y., Li, Z., Wu, W., and Zhu, C. (2018). New Approaches to Processing Ground-Based SAR (GBSAR) Data for Deformation Monitoring. Remote Sens., 10.","DOI":"10.3390\/rs10121936"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"719","DOI":"10.5721\/EuJRS20164937","article-title":"Micrometric deformation imaging at W-Band with GBSAR","volume":"19","author":"Martinez","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.isprsjprs.2014.04.002","article-title":"Discontinuous GBSAR deformation monitoring","volume":"93","author":"Crosetto","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"133802","DOI":"10.1109\/ACCESS.2020.3010584","article-title":"Multi-Phase-Center Sidelobe Suppression Method for Circular GBSAR Based on Sparse Spectrum","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.isprsjprs.2019.08.019","article-title":"Modelling of instrument repositioning errors in discontinuous Multi-Campaign Ground-Based SAR (MC-GBSAR) deformation monitoring","volume":"157","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Z., Liu, Y., Peng, J., and Mills, J. (2019). A New Processing Chain for Real-Time Ground-Based SAR (RT-GBSAR) Deformation Monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11202437"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5955","DOI":"10.1109\/TGRS.2020.2973533","article-title":"Iterative Atmospheric Phase Screen Compensation for Near-Real-Time Ground-Based InSAR Measurements Over a Mountainous Slope","volume":"58","author":"Izumi","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","first-page":"396","article-title":"Time series prediction method of large-scale surface subsidence based on deep learning","volume":"50","author":"Liu","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_19","first-page":"1804","article-title":"Slope of Large-scale Open-pit Mine Monitoring deformations by Using Ground-Based interferometry","volume":"27","author":"Yang","year":"2012","journal-title":"Prog. Geophys."},{"key":"ref_20","unstructured":"Liu, J. (2020). The Research of Atmospheric Correction Method for GB-InSAR, China University of Geosciences."},{"key":"ref_21","first-page":"41","article-title":"Monitoring of displacements with ground-based microwave interferometry: IBIS-S and IBIS-L","volume":"4","author":"Gerstenecker","year":"2010","journal-title":"J. Appl. Geod."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TGRS.2019.2940463","article-title":"A Multiple-Regression Model Considering Deformation Information for Atmospheric Phase Screen Compensation in Ground-Based SAR","volume":"58","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/LGRS.2010.2090647","article-title":"Atmospheric Phase Screen in Ground-Based Radar: Statistics and Compensation","volume":"8","author":"Iannini","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","first-page":"589","article-title":"A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI)","volume":"9","author":"Yu","year":"2005","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2286","DOI":"10.3390\/s22062286","article-title":"CM-LSTM Based Spectrum Sensing","volume":"22","author":"Wantong","year":"2022","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiao, F., Huang, L., Song, R., and Huang, H. (2021). An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic. Sensors, 21.","DOI":"10.3390\/s21175950"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neucom.2020.05.118","article-title":"DB-LSTM: Densely-connected Bi-directional LSTM for human action recognition","volume":"444","author":"He","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s12293-022-00355-y","article-title":"Multi-objective LSTM ensemble model for household short-term load forecasting","volume":"14","author":"Chaodong","year":"2022","journal-title":"Memetic Comput."},{"key":"ref_29","unstructured":"Liu, Y. (2021). Study on Monitoring Method of Surface Subsidence in Filling Mining Area Based on DS-InSAR, China University of Mining and Technology."},{"key":"ref_30","unstructured":"Du, J. (2021). Application of Kalman Filtering in GB-InSAR Slope Deformation Monitoring, China University of Geosciences."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5067\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:49:37Z","timestamp":1760143777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,11]]},"references-count":30,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205067"],"URL":"https:\/\/doi.org\/10.3390\/rs14205067","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,11]]}}}