{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:57:57Z","timestamp":1774493877423,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder\u2013decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones.<\/jats:p>","DOI":"10.3390\/rs14194956","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4361-0568","authenticated-orcid":false,"given":"Gianluca","family":"Murdaca","sequence":"first","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy"}]},{"given":"Alessio","family":"Rucci","sequence":"additional","affiliation":[{"name":"TRE ALTAMIRA s.r.l., 20143 Milano, Italy"}]},{"given":"Claudio","family":"Prati","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"ref_1","unstructured":"Prati, C., Rocca, F., Guarnieri, A.M., and Pasquali, P. (2022, September 25). Interferometric Techniques and Applications. ESA Study Contract Rep. Contract N.3- 7439\/92\/HGE-I, Ispra, Italy, 1994. Available online: https:\/\/esamultimedia.esa.int\/multimedia\/publications\/TM-19\/TM-19_InSAR_web.pdf."},{"key":"ref_2","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_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/TGRS.2012.2202911","article-title":"Directionally adaptive filter for synthetic aperture radar interferometric phase images","volume":"51","author":"Fu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/TGRS.2012.2234467","article-title":"Refined filtering of interferometric phase from InSAR data","volume":"51","author":"Chao","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/LGRS.2006.883527","article-title":"An adaptive contoured window filter for interferometric synthetic aperture radar","volume":"4","author":"Yu","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TGRS.2003.817212","article-title":"A modification to the Goldstein radar interferogram filter","volume":"41","author":"Baran","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/LGRS.2008.916066","article-title":"Noise Reduction in Interferograms Using the Wavelet Packet Transform and Wiener Filtering","volume":"5","author":"Zha","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","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":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Buades, A., Coll, B., and Morel, J.-M. (2005, January 20\u201325). A Non-Local Algorithm for Image Denoising. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1109\/TIP.2009.2029593","article-title":"Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights","volume":"18","author":"Deledalle","year":"2009","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_15","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":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_18","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_19","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_20","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":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1214\/aoms\/1177704250","article-title":"Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution (An Introduction)","volume":"34","author":"Goodman","year":"1963","journal-title":"Ann. Math. Stat."},{"key":"ref_22","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_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 Processing"},{"key":"ref_24","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201314). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy."},{"key":"ref_25","unstructured":"Iglewicz, B., and Hoaglin, D.C. (1993). How to Detect and Handle Outliers, ASQC Quality Press."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A. (2008, January 5\u20139). Extracting and Composing Robust Features with Denoising Autoencoders. Proceedings of the 25th International Conference on Machine Learning, ICML\u201908, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_27","first-page":"3371","article-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","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_29","first-page":"448","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume":"37","author":"Ioffe","year":"2015","journal-title":"ICML"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4956\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:31Z","timestamp":1760143591000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,4]]},"references-count":29,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194956"],"URL":"https:\/\/doi.org\/10.3390\/rs14194956","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,4]]}}}