{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:58:29Z","timestamp":1760230709068,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"],"award-info":[{"award-number":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"]}]},{"name":"Young Doctoral Fund Project of Higher Education Institutions in Gansu Province","award":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"],"award-info":[{"award-number":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"]}]},{"name":"State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR","award":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"],"award-info":[{"award-number":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"]}]},{"name":"Joint Innovation Fund of Lanzhou Jiaotong University and Tianjin University","award":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"],"award-info":[{"award-number":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"]}]},{"name":"Natural Science Foundation of Gansu Province","award":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"],"award-info":[{"award-number":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"]}]},{"name":"Jiayuguan City 2021 Science and Technology Plan Projects","award":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"],"award-info":[{"award-number":["KF-2021-06-014","2022QB-058","2022-03-03","2020055","20JR10RA249","437069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram\u2019s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method\u2019s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.<\/jats:p>","DOI":"10.3390\/s22165956","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T04:20:32Z","timestamp":1660105232000},"page":"5956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN"],"prefix":"10.3390","volume":"22","author":[{"given":"Wang","family":"Yang","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"given":"Yi","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0183-6064","authenticated-orcid":false,"given":"Sheng","family":"Yao","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"given":"Lifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"given":"Shengpeng","family":"Cao","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"given":"Zhiqing","family":"Wen","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112254","DOI":"10.1016\/j.rse.2020.112254","article-title":"Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico","volume":"254","author":"Cigna","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8512","DOI":"10.1109\/JSTARS.2021.3105231","article-title":"A Comparative Study of DEM Reconstruction Using the Single-Baseline and Multibaseline InSAR Techniques","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","first-page":"177","article-title":"InSAR monitoring of 3-D surface deformation in Jinchuan Mining area, Gansu Province","volume":"34","author":"Yang","year":"2022","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1016\/j.asr.2020.11.004","article-title":"TS-InSAR analysis for monitoring ground deformation in Lanzhou New District, the loess Plateau of China, from 2017 to 2019","volume":"67","author":"He","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TGRS.2019.2950353","article-title":"A sequential Monte Carlo framework for noise filtering in InSAR time series","volume":"58","author":"Khaki","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/36.175330","article-title":"Decorrelation in interferometric radar echoes","volume":"30","author":"Zebker","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Wang, Y. (2016). Study on High-Efficiency and High-Precision Filtering Methods for Synthetic Aperture Radar Interferometric Phase Images, National University of Defense Technology."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/36.536526","article-title":"Generation of digital elevation models by using SIR-C\/X-SAR multifrequency two-pass interferometry: The Etna case study","volume":"34","author":"Lanari","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/36.718849","article-title":"A new technique for noise filtering of SAR interferometric phase images","volume":"36","author":"Lee","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1109\/TGRS.2010.2076376","article-title":"NL-InSAR: Nonlocal interferogram estimation","volume":"49","author":"Deledalle","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3456","DOI":"10.1109\/TGRS.2018.2800087","article-title":"InSAR-BM3D: A nonlocal filter for SAR interferometric phase restoration","volume":"56","author":"Sica","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.image.2013.01.006","article-title":"Adaptive non-local means filter for image deblocking","volume":"28","author":"Wang","year":"2013","journal-title":"Signal Processing Image Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1109\/LGRS.2011.2158289","article-title":"An efficient and adaptive approach for noise filtering of SAR interferometric phase images","volume":"8","author":"Wang","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MGRS.2019.2955120","article-title":"InSAR phase denoising: A review of current technologies and future directions","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","unstructured":"Baier, G., Zhu, X.X., Lachaise, M., Breit, H., and Bamler, R. (2016, January 6\u20139). Nonlocal InSAR filtering for DEM generation and addressing the staircasing effect. Proceedings of the EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4035","DOI":"10.1029\/1998GL900033","article-title":"Radar interferogram filtering for geophysical applications","volume":"25","author":"Goldstein","year":"1998","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/36.729368","article-title":"Improving phase unwrapping techniques by the use of local frequency estimates","volume":"36","author":"Trouve","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","first-page":"980","article-title":"Global filter networks for image classification","volume":"34","author":"Rao","year":"2021","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.isprsjprs.2014.09.012","article-title":"A hybrid method for optimization of the adaptive Goldstein filter","volume":"98","author":"Jiang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1109\/TGRS.2010.2076286","article-title":"Interferometric SAR phase filtering in the wavelet domain using simultaneous detection and estimation","volume":"49","author":"Bian","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4947","DOI":"10.1109\/JSTARS.2020.3017808","article-title":"An interferometric phase noise reduction method based on modified denoising convolutional neural network","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1109\/TGRS.2020.3020427","article-title":"\u03a6-Net: Deep residual learning for InSAR parameters estimation","volume":"59","author":"Sica","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6544","DOI":"10.1109\/JSTARS.2021.3085397","article-title":"An extraction method for glacial lakes based on Landsat-8 im-agery using an improved U-Net network","volume":"14","author":"He","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4236\/jcc.2019.73002","article-title":"Image quality assessment through FSIM, SSIM, MSE and PSNR\u2014A comparative study","volume":"7","author":"Sara","year":"2019","journal-title":"J. Comput. Commun."},{"key":"ref_26","first-page":"102508","article-title":"A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping","volume":"104","author":"He","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.artint.2014.02.004","article-title":"The dropout learning algorithm","volume":"210","author":"Baldi","year":"2014","journal-title":"Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5082","DOI":"10.1109\/TNNLS.2020.3026784","article-title":"Revisiting internal covariate shift for batch normalization","volume":"32","author":"Awais","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pu, L., Zhang, X., Zhou, Z., Shi, J., Wei, S., and Zhou, Y. (2020). A Phase Filtering Method with Scale Recurrent Networks for InSAR. Remote Sens., 12.","DOI":"10.3390\/rs12203453"},{"key":"ref_32","first-page":"133","article-title":"The study of terrain simulation based on fractal","volume":"8","author":"Fang","year":"2009","journal-title":"WSEAS Trans. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1029\/2018EA000533","article-title":"Generative modeling of InSAR interferograms","volume":"6","author":"Rongier","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104331","DOI":"10.1016\/j.cageo.2019.104331","article-title":"Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction","volume":"133","author":"Zhang","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/TGRS.2008.2008095","article-title":"A particle filter approach for InSAR phase filtering and unwrapping","volume":"47","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Li, Q. (2020). Surface Subsidence Monitoring of Jinchuan Mining Area in Gansu Based on Space-Air-Ground Integration. [Master\u2019s Thesis, Southwest University of Science and Technology]."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MGRS.2019.2954395","article-title":"Entering the era of earth observation-based landslide warning systems: A novel and exciting framework","volume":"8","author":"Dai","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sun, Q., Ma, F., Guo, J., Li, G., and Feng, X. (2020). Deformation failure mechanism of deep vertical shaft in Jinchuan mining area. Sustainability, 12.","DOI":"10.3390\/su12062226"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gorbatsevich, V., Melnichenko, M., and Vygolov, O. (2019, January 21). Enhancing detail of 3D terrain models using GAN. Proceedings of the Modeling Aspects in Optical Metrology VII, Munich, Germany.","DOI":"10.1117\/12.2525177"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/5956\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:06:29Z","timestamp":1760141189000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/5956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22165956"],"URL":"https:\/\/doi.org\/10.3390\/s22165956","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,8,9]]}}}