{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:21:21Z","timestamp":1778692881656,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T00:00:00Z","timestamp":1645920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFB0502700"],"award-info":[{"award-number":["2017YFB0502700"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571099"],"award-info":[{"award-number":["61571099"]}],"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":["61501098"],"award-info":[{"award-number":["61501098"]}],"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":["61671113"],"award-info":[{"award-number":["61671113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder\u2013decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder\u2013decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods.<\/jats:p>","DOI":"10.3390\/rs14051174","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Nonlocal Feature Selection Encoder\u2013Decoder Network for Accurate InSAR Phase Filtering"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1197-3460","authenticated-orcid":false,"given":"Liming","family":"Pu","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2941-3357","authenticated-orcid":false,"given":"Liming","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4802-2447","authenticated-orcid":false,"given":"Liang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunjun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0266-5611\/14\/4\/001","article-title":"Synthetic aperture radar interferometry","volume":"14","author":"Bamler","year":"1998","journal-title":"Inverse Probl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhu, X., Wang, Y., Montazeri, S., and Ge, N. (2018). A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data. Remote Sens., 10.","DOI":"10.3390\/rs10091374"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Richter, N., and Froger, J.L. (2020). The role of Interferometric Synthetic Aperture Radar in detecting, mapping, monitoring, and modelling the volcanic activity of Piton de la Fournaise, La R\u00e9union: A review. Remote Sens., 12.","DOI":"10.3390\/rs12061019"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/JSTARS.2020.3036395","article-title":"Polarimetric Behavior for the Derivation of Sea Ice Topographic Height from TanDEM-X Interferometric SAR Data","volume":"14","author":"Huang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00190-021-01519-3","article-title":"Estimation of subcanopy topography based on single-baseline TanDEM-X InSAR data","volume":"95","author":"Wang","year":"2021","journal-title":"J. Geod."},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/TGRS.2005.864142","article-title":"Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation","volume":"44","author":"Vasile","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1109\/LGRS.2019.2951635","article-title":"Enhanced Interferometric Phase Noise Filtering of the Refined InSAR Filter","volume":"17","author":"Li","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/LGRS.2013.2263554","article-title":"Improved Goldstein SAR interferogram filter based on empirical mode decomposition","volume":"11","author":"Song","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6746","DOI":"10.1080\/01431161.2021.1944693","article-title":"An adaptive patch-based goldstein filter for interferometric phase denoising","volume":"42","author":"Chi","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"9756","DOI":"10.1109\/JSTARS.2021.3112588","article-title":"A Nonlocal Noise Reduction Method Based on Fringe Frequency Compensation for SAR Interferogram","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, X., Zimmer, A., Mukherjee, S., Kottayil, N.K., Ghuman, P., and Cheng, I. (2020). DeepInSAR\u2014A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation. Remote Sens., 12.","DOI":"10.3390\/rs12142340"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","first-page":"1087","article-title":"Neural Nearest Neighbors Networks","volume":"31","author":"Roth","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1109\/TGRS.2002.806997","article-title":"Modeling and reduction of SAR interferometric phase noise in the wavelet domain","volume":"40","author":"Fabregas","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"2802","article-title":"Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections","volume":"29","author":"Mao","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","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_23","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 Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pu, L., Zhang, X., Zhou, Z., Li, L., Zhou, L., Shi, J., and Wei, S. (2021). A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network. Remote Sens., 13.","DOI":"10.3390\/rs13224564"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4653","DOI":"10.1109\/TGRS.2020.2965918","article-title":"Deep Convolutional Neural Network-Based Robust Phase Gradient Estimation for Two-Dimensional Phase Unwrapping Using SAR Interferograms","volume":"58","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/TIP.2010.2052820","article-title":"Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content","volume":"19","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fang, D., Lv, X., Wang, Y., Lin, X., and Qian, J. (2016). A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage. Remote Sens., 8.","DOI":"10.3390\/rs8100830"},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:28:36Z","timestamp":1760135316000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,27]]},"references-count":28,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051174"],"URL":"https:\/\/doi.org\/10.3390\/rs14051174","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,27]]}}}