{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:08:16Z","timestamp":1773155296904,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61971430"],"award-info":[{"award-number":["61971430"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Inverse synthetic aperture radar (ISAR) imaging can be improved by processing Range-Instantaneous Doppler (RID) images, according to a method proposed in this paper that uses neural networks. ISAR is a significant imaging technique for moving targets. However, scatterers span across several range bins and Doppler bins while imaging a moving target over a large accumulated angle. Defocusing consequently occurs in the results produced by the conventional Range Doppler Algorithm (RDA). Defocusing can be solved with the time-frequency analysis (TFA) method, but the resolution performance is reduced. The proposed method provides the neural network with more details by using a string of RID frames of images as input. As a consequence, it produces better resolution and avoids defocusing. Furthermore, we have developed a positional encoding method that precisely represents pixel positions while taking into account the features of ISAR images. To address the issue of an imbalance in the ratio of pixel count between target and non-target areas in ISAR images, we additionally use the idea of Focal Loss to improve the Mean Squared Error (MSE). We conduct experiments with simulated data of point targets and full-wave simulated data produced by FEKO to assess the efficacy of the proposed approach. The experimental results demonstrate that our approach can improve resolution while preventing defocusing in ISAR images.<\/jats:p>","DOI":"10.3390\/rs15215166","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:20:07Z","timestamp":1698672007000},"page":"5166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep Learning-Based Enhanced ISAR-RID Imaging Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiurong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongpeng","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0031-3675","authenticated-orcid":false,"given":"Shaoqiu","family":"Song","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotao","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, S., Li, S., Jia, X., Cai, Y., and Liu, Y. (2022). An Efficient Translational Motion Compensation Approach for ISAR Imaging of Rapidly Spinning Targets. Remote Sens., 14.","DOI":"10.3390\/rs14092208"},{"key":"ref_2","first-page":"1","article-title":"A Novel ISAR Imaging Algorithm for Maneuvering Targets","volume":"19","author":"Zhu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, F., Huang, D., Guo, X., and Feng, C. (2022). Unambiguous ISAR Imaging Method for Complex Maneuvering Group Targets. Remote Sens., 14.","DOI":"10.3390\/rs14112554"},{"key":"ref_4","first-page":"1","article-title":"An Efficient ISAR Imaging Approach for Highly Maneuvering Targets Based on Subarray Averaging and Image Entropy","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"ISAR Imaging of a Maneuvering Target Based on Parameter Estimation of Multicomponent Cubic Phase Signals","volume":"60","author":"Huang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1109\/TAES.2016.150393","article-title":"Three-dimensional aircraft isar imaging based on shipborne radar","volume":"52","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, Z., Li, D., Tan, X., Liu, H., Liu, Y., and Liao, G. (2021). ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNR. Remote Sens., 13.","DOI":"10.3390\/rs13112198"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1109\/TAES.2010.5595608","article-title":"Estimation of Precession Parameters and Generation of ISAR Images of Ballistic Missile Targets","volume":"46","author":"Wang","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jin, X., Su, F., Li, H., Xu, Z., and Deng, J. (2023). Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm. Remote Sens., 15.","DOI":"10.3390\/rs15102507"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s00138-004-0140-y","article-title":"Ship identification in sequential ISAR imagery","volume":"15","author":"Maki","year":"2004","journal-title":"Mach. Vis. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3412582","DOI":"10.1155\/2020\/3412582","article-title":"A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions","volume":"2020","author":"Yang","year":"2020","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/TAES.2021.3136830","article-title":"3D-ISAR for an Along Track Airborne Radar","volume":"58","author":"Pui","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ni, P., Liu, Y., Pei, H., Du, H., Li, H., and Xu, G. (2022). CLISAR-Net: A Deformation-Robust ISAR Image Classification Network Using Contrastive Learning. Remote Sens., 15.","DOI":"10.3390\/rs15010033"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TAES.2018.2814211","article-title":"Classification of ISAR Images Using Variable Cross-Range Resolutions","volume":"54","author":"Lee","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/TAES.1980.308875","article-title":"Range-Doppler Imaging of Rotating Objects","volume":"16","author":"Walker","year":"1980","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1109\/TGRS.2020.3011638","article-title":"Orthorectified Polar Format Algorithm for Generalized Spotlight SAR Imaging with DEM","volume":"59","author":"Hu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, J., Li, Y., Yuan, Y., and Zhu, Y. (2023). Generalized Persistent Polar Format Algorithm for Fast Imaging of Airborne Video SAR. Remote Sens., 15.","DOI":"10.3390\/rs15112807"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.sigpro.2014.10.027","article-title":"High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction","volume":"108","author":"Sun","year":"2015","journal-title":"Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1109\/TAES.2018.2807283","article-title":"ISAR Image Resolution Enhancement: Compressive Sensing Versus State-of-the-Art Super-Resolution Techniques","volume":"54","author":"Giusti","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_20","first-page":"74","article-title":"Improvements of autofocusing techniques for ISAR motion compensation","volume":"24","author":"Zheng","year":"1996","journal-title":"Acta Electron. Sin."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1080\/01431161.2017.1415485","article-title":"ISAR imaging of complex motion targets based on Radon transform cubic chirplet decomposition","volume":"39","author":"Sun","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1109\/TAES.2018.2830598","article-title":"Bistatic ISAR Imaging and Scaling of Highly Maneuvering Target with Complex Motion via Compressive Sensing","volume":"54","author":"Kang","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1109\/TAES.2002.1008993","article-title":"Quantitative SNR analysis for ISAR imaging using joint time-frequency analysis-Short time Fourier transform","volume":"38","author":"Xia","year":"2002","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_24","first-page":"1","article-title":"Target Trajectory Estimation Algorithm Based on Time\u2013Frequency Enhancement","volume":"72","author":"Peng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1049\/iet-rsn:20080003","article-title":"New ISAR imaging algorithm based on modified Wigner-Ville distribution","volume":"3","author":"Xing","year":"2008","journal-title":"IET Radar Sonar Navig."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2015.2490582","article-title":"A Fast SAR Imaging Method for Ground Moving Target Using a Second-Order WVD Transform","volume":"54","author":"Huang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19953","DOI":"10.1109\/JSEN.2022.3202251","article-title":"Frame Selection Method for ISAR Imaging of 3-D Rotating Target Based on Time\u2013Frequency Analysis and Radon Transform","volume":"22","author":"Ryu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6395","DOI":"10.1109\/TGRS.2018.2838260","article-title":"Sea-Surface Floating Small Target Detection by One-Class Classifier in Time-Frequency Feature Space","volume":"56","author":"Shi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1109\/83.974573","article-title":"High-resolution ISAR imaging of maneuvering targets by means of the range instantaneous Doppler technique: Modeling and performance analysis","volume":"10","author":"Berizzi","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","first-page":"1","article-title":"Deep-Learning-Based BCI for Automatic Imagined Speech Recognition Using SPWVD","volume":"72","author":"Kamble","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"135","DOI":"10.5194\/isprs-archives-XLII-2-W16-135-2019","article-title":"Events Recognition for a Semi-Automatic Annotation of Soccer Videos: A Study Based Deep Learning","volume":"42","author":"Tani","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.ins.2021.08.019","article-title":"Deep Active Learning for Object Detection","volume":"579","author":"Li","year":"2021","journal-title":"Inform. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e553","DOI":"10.1016\/j.ijrobp.2022.07.2186","article-title":"Patient-Specific Auto-Segmentation of Target and OARs via Deep Learning on Daily Fan-Beam CT for Adaptive Prostate Radiotherapy","volume":"114","author":"Chen","year":"2022","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, W., Zhou, S., Yang, J., Gao, X., Ji, J., and Dong, C. (2023). Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System. Remote Sens., 15.","DOI":"10.3390\/rs15164068"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"419","DOI":"10.5194\/os-18-419-2022","article-title":"Forecasting hurricane-forced significant wave heights using a long short-term memory network in the Caribbean Sea","volume":"18","author":"Bethel","year":"2022","journal-title":"Ocean Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"680079","DOI":"10.3389\/fmars.2021.680079","article-title":"ConvLSTM-Based Wave Forecasts in the South and East China Seas","volume":"8","author":"Zhou","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Han, L., Ji, Q., Jia, X., Liu, Y., Han, G., and Lin, X. (2022). Significant Wave Height Prediction in the South China Sea Based on the ConvLSTM Algorithm. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10111683"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cen, H., Jiang, J., Han, G., Lin, X., Liu, Y., Jia, X., Ji, Q., and Li, B. (2022). Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea. Remote Sens., 14.","DOI":"10.3390\/rs14215461"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"672334","DOI":"10.3389\/fmars.2021.672334","article-title":"Application of Three Deep Learning Schemes Into Oceanic Eddy Detection","volume":"8","author":"Xu","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/LGRS.2020.2965743","article-title":"Enhancing ISAR Resolution by a Generative Adversarial Network","volume":"18","author":"Qin","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, K., Lu, X., Zhang, Q., Luo, Y., and Kang, L. (2022). ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network. Remote Sens., 14.","DOI":"10.3390\/rs14051291"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hu, C., Wang, L., Li, Z., and Loffeld, O. (2018, January 10\u201313). A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks. Proceedings of the 2018 5th International Workshop on Compressed Sensing Applied to Radar, Multimodal Sensing, and Imaging (CoSeRa), Siegen, Germany.","DOI":"10.1109\/RADAR.2018.8378712"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, X., Bai, X., and Zhou, F. (2021). High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net. Remote Sens., 13.","DOI":"10.3390\/rs13122326"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, X., Bai, X., Zhang, Y., and Zhou, F. (2022). High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net. Remote Sens., 14.","DOI":"10.3390\/rs14040901"},{"key":"ref_45","first-page":"1","article-title":"Real-Time Super-Resolution ISAR Imaging Using Unsupervised Learning","volume":"19","author":"Huang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","first-page":"1","article-title":"High-Resolution Refocusing for Defocused ISAR Images by Complex-Valued Pix2pixHD Network","volume":"19","author":"Yuan","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","first-page":"1","article-title":"Super-Resolution ISAR Imaging for Maneuvering Target Based on Deep-Learning-Assisted Time-Frequency Analysis","volume":"60","author":"Qian","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","first-page":"1","article-title":"AF-AMPNet: A Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing","volume":"60","author":"Wei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1109\/JSEN.2020.3025053","article-title":"Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing Based on Complex-Valued ADMM-Net","volume":"21","author":"Li","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_50","first-page":"28","article-title":"Wide-Angle ISAR Imaging Based on U-net Convolutional Neural Network","volume":"23","author":"Li","year":"2022","journal-title":"J. Air Force Eng. Univ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.23919\/JSEE.2020.000090","article-title":"ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform","volume":"31","author":"Shi","year":"2020","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/LGRS.2009.2030372","article-title":"On the Doppler Spreading Effect for the Range-Instantaneous-Doppler Technique in Inverse Synthetic Aperture Radar Imagery","volume":"7","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1049\/ip-rsn:19982220","article-title":"Time-varying spectral analysis for radar imaging of manoeuvring targets","volume":"145","author":"Chen","year":"1998","journal-title":"IEE Proc. Radar. Son. Nav."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1109\/TAES.2020.3040530","article-title":"Adaptive Clutter Suppression in Randomized Stepped-Frequency Radar","volume":"57","author":"Liu","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_55","first-page":"318","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"Facebook AI Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1049\/el:20080522","article-title":"Scope of validity of PSNR in image\/video quality assessment","volume":"44","author":"Ghanbari","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_57","unstructured":"The Communist Youth League of China, China Association for Science and Technology, Ministry of Education of the People\u2019s Republic of China, Chinese Academy of Social Sciences, and All-China Students\u2019 Federation (2023, October 19). \u201cChallenge Cup\u201d National Science and Technology College of Extra-Curricular Academic Competition Work. 8 June 2023. Available online: https:\/\/www.tiaozhanbei.net\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:13:47Z","timestamp":1760130827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,29]]},"references-count":57,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215166"],"URL":"https:\/\/doi.org\/10.3390\/rs15215166","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,29]]}}}