{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T11:22:04Z","timestamp":1780572124115,"version":"3.54.1"},"reference-count":58,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T00:00:00Z","timestamp":1636243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Shenzhen Science and Technology Program","award":["KQTD20180410161218820"],"award-info":[{"award-number":["KQTD20180410161218820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.<\/jats:p>","DOI":"10.3390\/rs13214468","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"4468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Yufang","family":"He","sequence":"first","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangzong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hermann","family":"Kaufmann","sequence":"additional","affiliation":[{"name":"School of Space Science and Physics, Shandong University, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guochang","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, B., Xu, G., Lu, Z., He, Y., Peng, M., and Feng, X. (2021). Coseismic Deformation Mechanisms of the 2021 Ms 6.4 Yangbi Earthquake, Yunnan Province, Using InSAR Observations. Remote Sens., 13.","DOI":"10.3390\/rs13193961"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2006.01.023","article-title":"A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data","volume":"102","author":"Casu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tecto.2011.10.013","article-title":"Recent advances in SAR interferometry time series analysis for measuring crustal deformation","volume":"514\u2013517","author":"Hooper","year":"2012","journal-title":"Tectonophysics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2006.11.015","article-title":"Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach","volume":"108","author":"Tizzani","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, F., Meng, X., Zhang, Y., Chen, G., Su, X., and Yue, D. (2019). Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology. Sensors, 19.","DOI":"10.3390\/s19122685"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8973","DOI":"10.3390\/rs70708973","article-title":"ALOS\/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan","volume":"7","author":"Teshebaeva","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.3390\/rs6021476","article-title":"Evaluation of InSAR and TomoSAR for Monitoring Deformations Caused by Mining in a Mountainous Area with High Resolution Satellite-Based SAR","volume":"6","author":"Liu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7253","DOI":"10.1038\/s41598-018-25369-w","article-title":"Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites","volume":"8","author":"Raspini","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3648","DOI":"10.3390\/rs6053648","article-title":"Long-Term Land Subsidence Monitoring of Beijing (China) Using the Small Baseline Subset (SBAS) Technique","volume":"6","author":"Hu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Casu, F., Lanari, R., Sansosti, E., Poland, M., Miklius, A., Solaro, G., and Tizzani, P. (2009, January 12\u201317). SBAS-InSAR analysis of surface deformation at Mauna Loa and Kilauea volcanoes in Hawaii. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417600"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.3390\/rs4092753","article-title":"Mapping of Ice Motion in Antarctica Using Synthetic-Aperture Radar Data","volume":"4","author":"Mouginot","year":"2012","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.rse.2011.10.020","article-title":"Mapping ground surface deformation using temporarily coherent point SAR interferometry: Application to Los Angeles Basin","volume":"117","author":"Zhang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","first-page":"482","article-title":"Monitoring urban subsidence based on SAR lnterferometric point target analysis","volume":"38","author":"Zhang","year":"2009","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1109\/TGRS.2010.2104325","article-title":"Deformation Time-Series Generation in Areas Characterized by Large Displacement Dynamics: The SAR Amplitude Pixel-Offset SBAS Technique","volume":"49","author":"Casu","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","unstructured":"Esfahany, S.S. (2017). Exploitation of Distributed Scatterers in Synthetic Aperture Radar Interferometry. [Ph.D. Thesis, Delft University of Technology]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1002\/2014JB011271","article-title":"A noise model for InSAR time series","volume":"120","author":"Agram","year":"2015","journal-title":"J. Geophys. Res. Solid Earth JGR"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s00190-017-1055-5","article-title":"Stochastic modeling for time series InSAR: With emphasis on atmospheric effects","volume":"92","author":"Cao","year":"2018","journal-title":"J. Geod."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3977","DOI":"10.1109\/TGRS.2019.2960007","article-title":"Distributed Scatterer Interferometry With the Refinement of Spatiotemporal Coherence","volume":"58","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"6662097","article-title":"Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data","volume":"2021","author":"Wang","year":"2021","journal-title":"Sci. Program."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4394","DOI":"10.1109\/TGRS.2015.2396875","article-title":"Improved EMCF-SBAS Processing Chain Based on Advanced Techniques for the Noise-Filtering and Selection of Small Baseline Multi-Look DInSAR Interferograms","volume":"53","author":"Pepe","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112898","DOI":"10.1109\/ACCESS.2020.3002990","article-title":"Adaptively Selecting Interferograms for SBAS-InSAR Based on Graph Theory and Turbulence Atmosphere","volume":"8","author":"Duan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.09.013","article-title":"Big Remotely Sensed Data: Tools, applications and experiences","volume":"202","author":"Casu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.aasri.2014.05.013","article-title":"Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks","volume":"6","author":"Smirnov","year":"2014","journal-title":"AASRI Procedia"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1109\/LGRS.2020.3010504","article-title":"An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation","volume":"18","author":"Mukherjee","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patrec.2021.01.010","article-title":"Deep learning for real-time semantic segmentation: Application in ultrasound imaging","volume":"144","author":"Ouahabi","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20704","DOI":"10.1109\/JSEN.2021.3100151","article-title":"Ear Recognition Based on Deep Unsupervised Active Learning","volume":"21","author":"Khaldi","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Adjabi, I., Ouahabi, A., Benzaoui, A., and Jacques, S. (2021). Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors, 21.","DOI":"10.3390\/s21030728"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Adjabi, I., Ouahabi, A., Benzaoui, A., and Taleb-Ahmed, A. (2020). Past, Present, and Future of Face Recognition: A Review. Electronics, 9.","DOI":"10.20944\/preprints202007.0479.v1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/TGRS.2020.3018315","article-title":"Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data","volume":"59","author":"Anantrasirichai","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.ecoinf.2019.01.009","article-title":"Habitat-Net: Segmentation of habitat images using deep learning","volume":"51","author":"Abrams","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2020JB019840","DOI":"10.1029\/2020JB019840","article-title":"Automatic Detection of Volcanic Surface Deformation Using Deep Learning","volume":"125","author":"Sun","year":"2020","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.cageo.2008.08.007","article-title":"Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat\u2014Turkey)","volume":"35","author":"Yilmaz","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity Mappings in Deep Residual Networks. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_36","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2021, October 25). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Available online: https:\/\/arxiv.org\/abs\/1602.07261."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Graupe, D. (2015). Deep Learning Neural Networks, Elsevier Science Ltd.","DOI":"10.1142\/10190"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3221\/IGF-ESIS.58.03","article-title":"Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete","volume":"15","author":"Hamiane","year":"2021","journal-title":"Frat. Integrit\u00e0 Strutt."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Arbaoui, A., Ouahabi, A., Jacques, S., and Hamiane, M. (2021). Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis. Electronics, 10.","DOI":"10.20944\/preprints202106.0194.v1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1007\/s00024-009-0491-4","article-title":"Analysis of Ground Deformation Detected Using the SBAS-DInSAR Technique in Umbria, Central Italy","volume":"166","author":"Guzzetti","year":"2009","journal-title":"Pure Appl. Geophys."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gee, D., Bateson, L., Sowter, A., Grebby, S., Novellino, A., Cigna, F., Marsh, S., Banton, C., and Wyatt, L. (2017). Ground Motion in Areas of Abandoned Mining: Application of the Intermittent SBAS (ISBAS) to the Northumberland and Durham Coalfield, UK. Geosciences, 7.","DOI":"10.3390\/geosciences7030085"},{"key":"ref_43","unstructured":"Goyal, P., Doll\u00e1r, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K. (2017). Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., and Zisserman, A. (2018, January 15\u201319). VGGFace2: A Dataset for Recognising Faces across Pose and Age. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00020"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/JSTSP.2017.2764438","article-title":"End-to-End Multimodal Emotion Recognition Using Deep Neural Networks","volume":"11","author":"Tzirakis","year":"2017","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/36.763299","article-title":"A neural-statistical approach to multitemporal and multisource remote-sensing image classification","volume":"37","author":"Bruzzone","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2001). Combining Classiers: Soft Computing Solutions, World Scientific.","DOI":"10.1142\/9789812386533_0015"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/S0041-624X(99)00113-4","article-title":"Harmonic propagation of finite amplitude sound beams: Experimental determination of the nonlinearity parameter B\/A","volume":"38","author":"Labat","year":"2000","journal-title":"Ultrasonics"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ouahabi, A. (2013, January 12\u201315). A review of wavelet denoising in medical imaging. Proceedings of the 2013 8th International Workshop on Systems, Signal Processing and Their Applications (WoSSPA), Algiers, Algeria.","DOI":"10.1109\/WoSSPA.2013.6602330"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.3390\/e17053461","article-title":"Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images","volume":"17","author":"Ahmed","year":"2015","journal-title":"Entropy"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hanssen, R.F. (2001). Radar Interferometry Data Interpretation and Error Analysis, Springer Science & Business Media.","DOI":"10.1007\/0-306-47633-9"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1109\/TGRS.2009.2034257","article-title":"Signatures of ERS\u2013Envisat Interferometric SAR Coherence and Phase of Short Vegetation: An Analysis in the Case of Maize Fields","volume":"48","author":"Santoro","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7075","DOI":"10.1080\/01431161.2012.700137","article-title":"Simulation of time-series surface deformation to validate a multi-interferogram InSAR processing technique","volume":"33","author":"Lee","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Xu, B., Feng, G., Li, Z., Wang, Q., Wang, C., and Xie, R. (2016). Coastal Subsidence Monitoring Associated with Land Reclamation Using the Point Target Based SBAS-InSAR Method: A Case Study of Shenzhen, China. Remote Sens., 8.","DOI":"10.3390\/rs8080652"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, P., Chen, X., Li, Z., Zhang, Z., Xu, J., Feng, W., Wang, C., Hu, Z., Tu, W., and Li, H. (2018). Resolving Surface Displacements in Shenzhen of China from Time Series InSAR. Remote Sens., 10.","DOI":"10.3390\/rs10071162"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"He, Y., Xu, G., Kaufmann, H., Wang, J., Ma, H., and Liu, T. (2021). Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China. Remote Sens., 13.","DOI":"10.3390\/rs13122366"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Lin, H., Chen, F., and Gao, W. (2011, January 24\u201326). InSAR detection of residual settlement of ocean reclamation areas in Shenzhen, China. Proceedings of the 2011 19th International Conference on Geoinformatics, Shanghai, China.","DOI":"10.1109\/GeoInformatics.2011.5980766"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lu, D., Toro, F.G., and Cai, B. (2015, January 19\u201323). Methods for certification of GNSS-based safe vehicle localisation i. Proceedings of the IEEE 2015 International Conference on Connected Vehicles and Expo (ICCVE), Shenzhen, China.","DOI":"10.1109\/ICCVE.2015.19"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4468\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:27:03Z","timestamp":1760167623000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,7]]},"references-count":58,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214468"],"URL":"https:\/\/doi.org\/10.3390\/rs13214468","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,7]]}}}