{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:05:54Z","timestamp":1780337154843,"version":"3.54.1"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the problems of extremely scarce measured samples and significant domain shift between simulated and measured data in automatic target recognition (ATR) of air targets for spaceborne radar, this paper proposes an inverse synthetic aperture radar (ISAR) image recognition method for air targets combining physics-driven data augmentation guided by detection prior information with domain adversarial transfer learning. First, the mapping relationship between scattering point projection and ISAR images is established by using the target 3D point cloud and radar observation geometric priors, and a 2D sinc kernel function is introduced for energy distribution rendering. Then, under the unsupervised transfer learning paradigm, aiming at the distribution inconsistency between augmented data (source domain) and unlabeled simulated data (target domain), this paper designs a cross-domain recognition task experiment including six types of typical aircraft targets, and compares the cross-domain recognition performance of three transfer learning methods (model fine-tuning, deep domain confusion (DDC) and domain-adversarial neural networks (DANN)) on the target domain. Meanwhile, t-distributed stochastic neighbor embedding (t-SNE) visualization is used to analyze the feature distribution alignment ability of the models. Simulation experiments show that the DANN model with a dynamic inversion coefficient introduced in the gradient reversal layer (GRL) achieves a recognition accuracy of 99.5% on the unlabeled target domain, which is significantly superior to the model fine-tuning and DDC methods. Moreover, it makes the feature distributions of source and target domain samples highly overlapping, and maintains a strong inter-class discriminability while eliminating the domain shift. The proposed scheme provides a physically interpretable and robust technical path for few-shot radar target image recognition.<\/jats:p>","DOI":"10.3390\/s26113323","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T01:45:08Z","timestamp":1779673508000},"page":"3323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Air Target ISAR Recognition Based on Data Augmentation and Transfer Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Moqian","family":"Wang","sequence":"first","affiliation":[{"name":"Nanjing Research Institute of Electronics Technology, Nanjing 210039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0444-9297","authenticated-orcid":false,"given":"Zuzhen","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronics Technology, Nanjing 210039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjian","family":"Cai","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronics Technology, Nanjing 210039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronics Technology, Nanjing 210039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youquan","family":"Lin","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronics Technology, Nanjing 210039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"ref_1","first-page":"7","article-title":"Development history and current situation analysis of U.S. space-based early warning radar","volume":"40","author":"Li","year":"2018","journal-title":"Mod. Radar"},{"key":"ref_2","first-page":"1054","article-title":"Development status and key technologies of foreign space-based early warning radar systems","volume":"52","author":"Zhu","year":"2012","journal-title":"Telecommun. Eng."},{"key":"ref_3","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, Curran Associates."},{"key":"ref_4","first-page":"10","article-title":"Overview of the Application of Generative Adversarial Networks in SAR Image Processing","volume":"45","author":"Gao","year":"2024","journal-title":"J. Ordnance Equip. Eng."},{"key":"ref_5","first-page":"15","article-title":"Research on image data augmentation technology","volume":"44","author":"Jiang","year":"2024","journal-title":"Ship Electron. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_7","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"487","DOI":"10.11834\/jig.200089","article-title":"A review of deep learning image data augmentation methods","volume":"26","author":"Ma","year":"2021","journal-title":"J. Image Graph."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/8.785750","article-title":"A parametric model for synthetic aperture radar measurements","volume":"47","author":"Gerry","year":"1999","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_11","first-page":"31","article-title":"A review of SAR data augmentation based on GAN","volume":"42","author":"Huang","year":"2021","journal-title":"J. Ordnance Equip. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/83.552098","article-title":"Attributed scattering centers for SAR ATR","volume":"6","author":"Potter","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P.S. (1993). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, IEEE.","DOI":"10.1109\/ACSSC.1993.342465"},{"key":"ref_14","first-page":"75","article-title":"SAR target recognition using deep convolutional neural networks in transfer learning mode","volume":"35","author":"Li","year":"2018","journal-title":"J. Univ. Chin. Acad. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5235415","DOI":"10.1109\/TGRS.2022.3208333","article-title":"Electromagnetic scattering feature (ESF) module embedded network based on ASC model for robust and interpretable SAR ATR","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.1109\/JSTARS.2017.2671919","article-title":"Target recognition in synthetic aperture radar images via matching of attributed scattering centers","volume":"10","author":"Ding","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","first-page":"228","article-title":"MSTAR extended operating conditions: A tutorial","volume":"Volume 2757","author":"Keydel","year":"1996","journal-title":"Algorithms for Synthetic Aperture Radar Imagery III"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_19","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv."},{"key":"ref_20","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target classification using the deep convolutional networks for SAR images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5226517","DOI":"10.1109\/TGRS.2022.3166174","article-title":"SCAN: Scattering characteristics analysis network for few-shot aircraft classification in high-resolution SAR images","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3323\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:11:45Z","timestamp":1780333905000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,23]]},"references-count":25,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26113323"],"URL":"https:\/\/doi.org\/10.3390\/s26113323","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,23]]}}}