{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T02:08:47Z","timestamp":1769306927719,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T00:00:00Z","timestamp":1562803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M613076 and 2016M602775"],"award-info":[{"award-number":["2017M613076 and 2016M602775"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801347, 61801344, 61522114, 61471284, 61571349, 61631019, and 61801390"],"award-info":[{"award-number":["61801347, 61801344, 61522114, 61471284, 61571349, 61631019, and 61801390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010906","name":"NSAF Joint Fund","doi-asserted-by":"publisher","award":["U1430123"],"award-info":[{"award-number":["U1430123"]}],"id":[{"id":"10.13039\/501100010906","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XJS17070, NSIY031403, and 3102017jg02014"],"award-info":[{"award-number":["XJS17070, NSIY031403, and 3102017jg02014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2018JM6051"],"award-info":[{"award-number":["2018JM6051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radio Frequency Interference (RFI) is a key issue for Synthetic Aperture Radar (SAR) because it can seriously degrade the imaging quality, leading to the misinterpretation of the target scattering characteristics and hindering the subsequent image analysis. To address this issue, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on deep residual network (ResNet). First, the short-time Fourier transform (STFT) is used to characterize the interference-corrupted echo in the time\u2013frequency domain. Then, the interference detection model is built by a classical deep convolutional neural network (DCNN) framework to identify whether there is an interference component in the echo. Furthermore, the time\u2013frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time\u2013frequency Fourier transform (ISTFT) is utilized to transform the time\u2013frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulated and measured SAR data with strip mode and terrain observation by progressive scans (TOPS) mode. Moreover, in comparison with the notch filtering and the eigensubspace filtering, the proposed interference mitigation algorithm can improve the interference mitigation performance, while reducing the computation complexity.<\/jats:p>","DOI":"10.3390\/rs11141654","type":"journal-article","created":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T11:28:28Z","timestamp":1562844508000},"page":"1654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Weiwei","family":"Fan","sequence":"first","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Feng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0329-7124","authenticated-orcid":false,"given":"Mingliang","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China"}]},{"given":"Xueru","family":"Bai","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Pengshuai","family":"Rong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Shuang","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Tian","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/JPROC.2012.2220511","article-title":"Very-high-resolution airborne synthetic aperture radar imaging: signal processing and applications","volume":"101","author":"Reigber","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dudczyk, J., Kawalec, A., and Cyrek, J. (2008, January 21\u201323). Applying the distance and similarity functions to radar signals identification. Proceedings of the 2008 International Radar Symposium, Wroclaw, Poland.","DOI":"10.1109\/IRS.2008.4585771"},{"key":"ref_4","first-page":"511","article-title":"Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar","volume":"62","author":"Dudczyk","year":"2014","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Matuszewski, J. (2018, January 20\u201324). Radar signal identification using a neural network and pattern recognition methods. Proceedings of the 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine.","DOI":"10.1109\/TCSET.2018.8336160"},{"key":"ref_6","unstructured":"Kim, A., Dogan, S., Fisher, J., Moses, R., and Willsky, A. (2000, January 24\u201328). Attributing scatterer anisotropy for model based ATR. Proceedings of the International Society for Optical Engineering, Orlando, FL, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sadjadi, A. New experiments in inverse synthetic aperture radar image exploitation for maritime surveillance, In Proceedings of the International Society for Optical Engineering, Baltimore, MD, USA, 5\u20136 May 2014.","DOI":"10.1117\/12.2053797"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1109\/TGRS.2013.2252469","article-title":"Correction and characterization of radio frequency interference signatures in l-band syntheticaperture radar data","volume":"51","author":"Meyer","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5016","DOI":"10.1109\/JSTARS.2017.2727520","article-title":"Narrow-band interference suppression via rpca-based signal separation in time\u2013frequency domain","volume":"10","author":"Su","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","first-page":"3202","article-title":"Narrow-band interference suppression for sar based on complex empirical mode decomposition","volume":"50","author":"Zhou","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3476","DOI":"10.1109\/JSTARS.2015.2431916","article-title":"Research on methods for narrow-band interference suppression in synthetic aperture radar data","volume":"8","author":"Zhou","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TGRS.2015.2450754","article-title":"Wideband interference mitigation in high-resolution airborne synthetic aperture radar data","volume":"54","author":"Tao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Su, J., Tao, H., Tao, M., Xie, J., Wang, Y., and Wang, L. (2018). Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm. Remote Sens., 10.","DOI":"10.3390\/rs10091491"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, J., Li, J., Sun, B., Chen, J., and Li, C. (2018). Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector. Sensors, 18.","DOI":"10.3390\/s18114034"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1117\/12.603773","article-title":"Suppression of radio frequency interference (RFI) for synchronous impulse reconstruction ultra-wideband radar","volume":"5808","author":"Nguyen","year":"2005","journal-title":"Proc. SPIE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1109\/TGRS.2012.2210903","article-title":"Computationally efficient RF interference suppression method with closed-form maximum likelihoodestimator for HF surface wave over-the-horizon radars","volume":"51","author":"Yi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"33","article-title":"RFI suppression for synchronous impulse reconstruction UWB radar using RELAX","volume":"3","author":"Ojowu","year":"2013","journal-title":"Int. J. Remote Sens. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhou, F., Tao, M., and Sheng, M. (2017, January 19\u201326). A new method for sar radio frequency interference mitigation based on maximum a posterior estimation. Proceedings of the 2017 32nd General Assembly and Scientific Symposium of the International Union of Radio Science, Montreal, QC, Canada.","DOI":"10.23919\/URSIGASS.2017.8104495"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/LGRS.2004.838419","article-title":"Interference suppression in synthesized sar images","volume":"2","author":"Reigber","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1049\/ip-rsn:20050092","article-title":"Filtering approaches for interference suppression in low-frequency sar","volume":"153","author":"Smith","year":"2006","journal-title":"IEE Radar Sonar Navig."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/LGRS.2006.887033","article-title":"Eigensubpace-based filtering with application in narrow-band interference suppression for sar","volume":"4","author":"Zhou","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1049\/el.2011.3935","article-title":"RFI suppression in SAR based on approximate spectral decomposition algorithm","volume":"48","author":"Wang","year":"2012","journal-title":"Electron. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/LGRS.2011.2163150","article-title":"Application of subband spectral cancellation for sar narrow-band interference suppression","volume":"9","author":"Feng","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1109\/TGRS.2013.2253472","article-title":"RFI characterization and mitigation for the smap radar","volume":"51","author":"Spencer","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1109\/TGRS.2017.2782682","article-title":"Narrowband RFI suppression for sar system via fast implementation of joint sparsity and low-rank property","volume":"56","author":"Huang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1109\/TGRS.2018.2797946","article-title":"Narrowband RFI suppression for sar system via efficient parameter-free decomposition algorithm","volume":"56","author":"Huang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. In Proceeding of the 2012 Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_28","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 2015 International Conference Learning Representations (ICLR), New York, NY, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, S., and Farhadi, A. (July, January 26). You only look once: unified, real-time object detection. Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dronner, J., Korfhage, N., Egli, S., Muhling, M., Thies, B., Bendix, J., Freisleben, B., and Seeger, B. (2018). Fast cloud segmentation using convolutional neural networks. Remote Sens., 10.","DOI":"10.3390\/rs10111782"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 11\u201318). Learning deconvolution network for semantic segmentation. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, H., and Patel, V. (2017, January 10\u201313). Generative adversarial network-based restoration of speckled SAR images. Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Netherlan.","DOI":"10.1109\/CAMSAP.2017.8313133"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Michelsanti, D., and Tan, Z. (2017, January 20\u201324). Conditional generative adversarial networks for speech enhancement and noise-robust speaker verification. Proceedings of the Annual Conference of the International Speech Communication Association, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-1620"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/TCSVT.2018.2821177","article-title":"Multi-focus image fusion with a natural enhancement via joint multi-level deeply supervised convolutional neural network","volume":"29","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_40","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201311). Generative adversarial networks. In Proceeding of the 2014 Advances in Neural Information Processing Systems (NIPS), Montreal, AB, Canada."},{"key":"ref_41","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1109\/TIP.2018.2836316","article-title":"Perceptual adversarial networks for image-to-image transformation","volume":"27","author":"Wang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. Tensorflow: A system for large-scale machine learning. Proceedings of the 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), Savannah, GA, USA."},{"key":"ref_45","unstructured":"Kingma, D., and Adam, B. (2014, January 14\u201316). A method for stochastic optimization. Proceedings of the 2014 International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.optcom.2014.12.032","article-title":"Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition","volume":"341","author":"Cui","year":"2015","journal-title":"Opt. Commun."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/j.compeleceng.2009.02.001","article-title":"Gray level difference-based transition region extraction and thresholding","volume":"35","author":"Li","year":"2009","journal-title":"Comput. Electr. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1109\/TGRS.2006.873853","article-title":"Topsar: terrain observation by progressive scans","volume":"44","author":"Zan","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:04:40Z","timestamp":1760187880000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,11]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141654"],"URL":"https:\/\/doi.org\/10.3390\/rs11141654","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,11]]}}}