{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:29:26Z","timestamp":1775143766177,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42230109"],"award-info":[{"award-number":["42230109"]}]},{"name":"National Natural Science Foundation of China","award":["42101450"],"award-info":[{"award-number":["42101450"]}]},{"name":"National Natural Science Foundation of China","award":["202101BE070001-037"],"award-info":[{"award-number":["202101BE070001-037"]}]},{"name":"National Natural Science Foundation of China","award":["202201AU070104"],"award-info":[{"award-number":["202201AU070104"]}]},{"name":"National Natural Science Foundation of China","award":["KUST-xk2022005"],"award-info":[{"award-number":["KUST-xk2022005"]}]},{"name":"Yunnan Fundamental Research Projects","award":["42230109"],"award-info":[{"award-number":["42230109"]}]},{"name":"Yunnan Fundamental Research Projects","award":["42101450"],"award-info":[{"award-number":["42101450"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202101BE070001-037"],"award-info":[{"award-number":["202101BE070001-037"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202201AU070104"],"award-info":[{"award-number":["202201AU070104"]}]},{"name":"Yunnan Fundamental Research Projects","award":["KUST-xk2022005"],"award-info":[{"award-number":["KUST-xk2022005"]}]},{"name":"Interdisciplinary Research Project of KUST","award":["42230109"],"award-info":[{"award-number":["42230109"]}]},{"name":"Interdisciplinary Research Project of KUST","award":["42101450"],"award-info":[{"award-number":["42101450"]}]},{"name":"Interdisciplinary Research Project of KUST","award":["202101BE070001-037"],"award-info":[{"award-number":["202101BE070001-037"]}]},{"name":"Interdisciplinary Research Project of KUST","award":["202201AU070104"],"award-info":[{"award-number":["202201AU070104"]}]},{"name":"Interdisciplinary Research Project of KUST","award":["KUST-xk2022005"],"award-info":[{"award-number":["KUST-xk2022005"]}]},{"name":"the Platform Construction Project of High-Level Talent in KUST","award":["42230109"],"award-info":[{"award-number":["42230109"]}]},{"name":"the Platform Construction Project of High-Level Talent in KUST","award":["42101450"],"award-info":[{"award-number":["42101450"]}]},{"name":"the Platform Construction Project of High-Level Talent in KUST","award":["202101BE070001-037"],"award-info":[{"award-number":["202101BE070001-037"]}]},{"name":"the Platform Construction Project of High-Level Talent in KUST","award":["202201AU070104"],"award-info":[{"award-number":["202201AU070104"]}]},{"name":"the Platform Construction Project of High-Level Talent in KUST","award":["KUST-xk2022005"],"award-info":[{"award-number":["KUST-xk2022005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention.<\/jats:p>","DOI":"10.3390\/rs16010054","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T04:44:40Z","timestamp":1703220280000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Trend Classification of InSAR Displacement Time Series Using SAE\u2013CNN"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8027-4866","authenticated-orcid":false,"given":"Menghua","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}]},{"given":"Hanfei","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1449-3494","authenticated-orcid":false,"given":"Mengshi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Yunnan University, Kunming 650500, China"}]},{"given":"Cheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Kunming 650216, China"},{"name":"Yunnan Institute of Geological Environment Monitoring, Kunming 650216, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-5346","authenticated-orcid":false,"given":"Bo-Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2015.10.011","article-title":"Persistent Scatterer Interferometry: A review","volume":"115","author":"Crosetto","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.geog.2021.09.007","article-title":"Review of the SBAS InSAR Time-series algorithms, applications, and challenges","volume":"13","author":"Li","year":"2022","journal-title":"Geod. Geodyn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107069","DOI":"10.1016\/j.enggeo.2023.107069","article-title":"Complex surface displacements of the Nanyu landslide in Zhouqu, China revealed by multi-platform InSAR observations","volume":"317","author":"Li","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"113150","DOI":"10.1016\/j.rse.2022.113150","article-title":"A PSI targets characterization approach to interpreting surface displacement signals: A case study of the Shanghai metro tunnels","volume":"280","author":"Yang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"643","DOI":"10.5194\/npg-19-643-2012","article-title":"Semi-automated extraction of Deviation Indexes (DI) from satellite Persistent Scatterers time series: Tests on sedimentary volcanism and tectonically-induced motions","volume":"19","author":"Cigna","year":"2012","journal-title":"Nonlinear Process Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.5194\/nhess-13-1945-2013","article-title":"Automated classification of Persistent Scatterers Interferometry time series","volume":"13","author":"Berti","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s10346-015-0589-y","article-title":"Using wavelet tools to analyse seasonal variations from InSAR time-series data: A case study of the Huangtupo landslide","volume":"13","author":"Li","year":"2016","journal-title":"Landslides"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TGRS.2015.2459037","article-title":"A Probabilistic Approach for InSAR Time-Series Postprocessing","volume":"54","author":"Chang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bovenga, F., Pasquariello, G., and Refice, A. (2021). Statistically-Based Trend Analysis of MTInSAR Displacement Time Series. Remote Sens., 13.","DOI":"10.3390\/rs13122302"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2020.3031655","article-title":"Model-Free Characterization of SAR MTI Time Series","volume":"19","author":"Refice","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2020GC009204","DOI":"10.1029\/2020GC009204","article-title":"Identification of Surface Deformation in InSAR Using Machine Learning","volume":"22","author":"Brengman","year":"2021","journal-title":"Geochem. Geophys. Geosystems"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1038\/s41467-021-26254-3","article-title":"Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning","volume":"12","author":"Jolivet","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105317","DOI":"10.1016\/j.catena.2021.105317","article-title":"Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques","volume":"203","author":"Novellino","year":"2021","journal-title":"Catena"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3336053","article-title":"InSAR Spatial-Heterogeneity Tropospheric Delay Correction in Steep Mountainous Areas Based on Deep Learning for Landslides Monitoring","volume":"61","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4706512","DOI":"10.1109\/TGRS.2022.3169455","article-title":"ALADDIn: Autoencoder-LSTM-Based Anomaly Detector of Deformation in InSAR","volume":"60","author":"Shakeel","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ansari, H., Rubwurm, M., Ali, M., Montazeri, S., Parizzi, A., and Zhu, X.X. (2021, January 11\u201316). InSAR Displacement Time Series Mining: A Machine Learning Approach. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553465"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Martin, G., Selvakumaran, S., Marinoni, A., Sadeghi, Z., and Middleton, C. (2021, January 11\u201316). Structural Health Monitoring on Urban Areas by Using Multi Temporal Insar and Deep Learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554639"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/TGRS.2019.2945370","article-title":"Individual Scatterer Model Learning for Satellite Interferometry","volume":"58","author":"Pankratius","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","first-page":"103276","article-title":"Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering","volume":"118","author":"Festa","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rygus, M., Novellino, A., Hussain, E., Syafiudin, F., Andreas, H., and Meisina, C. (2023). A clustering approach for the analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia). Remote Sens., 15.","DOI":"10.3390\/rs15153776"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4559","DOI":"10.1109\/JSTARS.2022.3180994","article-title":"Use of LSTM for sinkhole-related anomaly detection and classification of InSAR deformation time series","volume":"15","author":"Kulshrestha","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mirmazloumi, S.M., Gambin, A.F., Palam\u00e0, R., Crosetto, M., Wassie, Y., Navarro, J.A., Barra, A., and Monserrat, O. (2022). Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data. Remote Sens., 14.","DOI":"10.3390\/rs14153821"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1080\/15481603.2022.2030535","article-title":"Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series","volume":"59","author":"Mirmazloumi","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2020.04.105","article-title":"An SAE-based resampling SVM ensemble learning paradigm for pipeline leakage detection","volume":"403","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104281","DOI":"10.1109\/ACCESS.2020.2999915","article-title":"Automatic identification of insomnia based on single-channel EEG labelled with sleep stage annotations","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","first-page":"14","article-title":"Restructuring batch normalization to accelerate CNN training","volume":"1","author":"Jung","year":"2019","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_28","first-page":"39","article-title":"Evaluation of classification models in machine learning","volume":"7","year":"2017","journal-title":"Theory Appl. Math. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e15285","DOI":"10.7717\/peerj.15285","article-title":"Spatio-temporal evolution and prediction of carbon storage in Kunming based on PLUS and InVEST models","volume":"11","author":"Li","year":"2023","journal-title":"PeerJ"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, W., Li, W.L., Zhang, Q., Yang, Y., Zhang, Y., Qu, W., and Wang, C.S. (2019). A Decade of Ground Deformation in Kunming (China) Revealed by Multi-Temporal Synthetic Aperture Radar Interferometry (InSAR) Technique. Sensors, 19.","DOI":"10.3390\/s19204425"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, M., Yin, X., Tang, B.-H., and Yang, M. (2023). Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat\/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China. Remote Sens., 15.","DOI":"10.3390\/rs15071952"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2004GL021737","article-title":"A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers","volume":"31","author":"Hooper","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","first-page":"2156","article-title":"Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volc\u00e1n Alcedo, Gal\u00e1pagos","volume":"2156\u20132202","author":"Hooper","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"666","DOI":"10.2112\/JCR-SI115-173.1","article-title":"Mechanism of Land Subsidence of Plateau Lakeside Kunming City Cluster (China) by MT-InSAR and Leveling Survey","volume":"115","author":"Li","year":"2020","journal-title":"J. Coast. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, M., Yang, M., and Tang, B.-H. (2022). Deformation Detection and Attribution Analysis of Urban Areas near Dianchi Lake in Kunming Using the Time-Series InSAR Technique. Appl. Sci., 12.","DOI":"10.3390\/app121910004"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shao, J., Li, J., and Yang, K. (2018, January 19\u201321). Time-Series Analysis of Land Subsidence in Kunming. Proceedings of the 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), Harbin, China.","DOI":"10.1109\/IMCCC.2018.00069"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1038\/s41467-019-13055-y","article-title":"Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets","volume":"10","author":"Belkina","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_38","first-page":"460","article-title":"The Study on Land Subsidence in Kunming by Integrating PS, SBAS and DS InSAR","volume":"37","author":"Guo","year":"2022","journal-title":"Remote Sens.Technol. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:15Z","timestamp":1760132415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010054"],"URL":"https:\/\/doi.org\/10.3390\/rs16010054","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,22]]}}}