{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:45:11Z","timestamp":1778694311052,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Foundation of China","award":["61771362"],"award-info":[{"award-number":["61771362"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the remote sensing image processing field, the synthetic aperture radar (SAR) target-detection methods based on convolutional neural networks (CNNs) have gained remarkable performance relying on large-scale labeled data. However, it is hard to obtain many labeled SAR images. Semi-supervised learning is an effective way to address the issue of limited labels on SAR images because it uses unlabeled data. In this paper, we propose an improved faster regions with CNN features (R-CNN) method, with a decoding module and a domain-adaptation module called FDDA, for semi-supervised SAR target detection. In FDDA, the decoding module is adopted to reconstruct all the labeled and unlabeled samples. In this way, a large number of unlabeled SAR images can be utilized to help structure the latent space and learn the representative features of the SAR images, devoting attention to performance promotion. Moreover, the domain-adaptation module is further introduced to utilize the unlabeled SAR images to promote the discriminability of features with the assistance of the abundantly labeled optical remote sensing (ORS) images. Specifically, the transferable features between the ORS images and SAR images are learned to reduce the domain discrepancy via the mean embedding matching, and the knowledge of ORS images is transferred to the SAR images for target detection. Ultimately, the joint optimization of the detection loss, reconstruction, and domain adaptation constraints leads to the promising performance of the FDDA. The experimental results on the measured SAR image datasets and the ORS images dataset indicate that our method achieves superior SAR target detection performance with limited labeled SAR images.<\/jats:p>","DOI":"10.3390\/rs14010143","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T23:31:35Z","timestamp":1640820695000},"page":"143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Semi-Supervised SAR Target Detection Based on an Improved Faster R-CNN"],"prefix":"10.3390","volume":"14","author":[{"given":"Leiyao","family":"Liao","sequence":"first","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-0022","authenticated-orcid":false,"given":"Lan","family":"Du","sequence":"additional","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3424-7231","authenticated-orcid":false,"given":"Yuchen","family":"Guo","sequence":"additional","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","article-title":"Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images","volume":"16","author":"Lin","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., and Shao, J. (2017, January 13\u201314). Ship detection in SAR images based on an improved faster R-CNN. Proceedings of the 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China.","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1109\/JSEN.2018.2791947","article-title":"Target Discrimination for SAR ATR Based on Scattering Center Feature and K-center One-Class Classification","volume":"18","author":"Li","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_5","first-page":"1","article-title":"Target discrimination method for SAR images based on semisupervised co-training","volume":"12","author":"Wang","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/LGRS.2016.2608578","article-title":"SAR Automatic Target Recognition Based on Dictionary Learning and Joint Dynamic Sparse Representation","volume":"13","author":"Sun","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3323","DOI":"10.1109\/JSTARS.2017.2670083","article-title":"SAR automatic target recognition based on Euclidean distance restricted autoencoder","volume":"10","author":"Deng","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images","volume":"47","author":"Gao","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/7.249129","article-title":"Optimal polarimetric processing for enhanced target detection","volume":"29","author":"Novak","year":"1993","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","article-title":"Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images","volume":"57","author":"Cui","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rosenberg, C., Hebert, M., and Schneiderman, H. (2005, January 5\u20137). Semi-Supervised Self-Training of Object Detection Models. Proceedings of the 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV\/MOTION\u201905), Washington, DC, USA.","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5553","DOI":"10.1109\/TGRS.2016.2569141","article-title":"Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., and Pfister, T. (2020). A simple semi-supervised learning framework for object detection. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wei, D., Du, Y., Du, L., and Li, L. (2021). Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism. Remote Sens., 13.","DOI":"10.3390\/rs13142686"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rahimzad, M., Homayouni, S., Naeini, A.A., and Nadi, S. (2021). An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE). Remote Sens., 13.","DOI":"10.3390\/rs13132501"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Protopapadakis, E., Doulamis, A., Doulamis, N., and Maltezos, E. (2021). Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13030371"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_18","unstructured":"Rodriguez, A.L., and Mikolajczyk, K. (2019). Domain adaptation for object detection via style consistency. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, C., Zheng, Z., Ding, X., Huang, Y., and Dou, Q. (2020, January 14\u201319). Harmonizing transferability and discriminability for adapting object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00889"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018, January 18\u201323). Domain adaptive faster r-cnn for object detection in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, Y., Du, L., and Lyu, G. (2021). SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size. Remote Sens., 13.","DOI":"10.3390\/rs13214202"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, L., Wei, W., and Zhang, Y. (2018). When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature. Remot. Sens., 10.","DOI":"10.3390\/rs10020284"},{"key":"ref_23","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_24","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M. (2015, January 6\u201311). Learning transferable features with deep adaptation networks. Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_26","unstructured":"Gutierrez, D. (2021, October 20). MiniSAR: A Review of 4-Inch and 1-Foot Resolution Ku-Band Imagery [EB\/OL], Available online: https:\/\/www.sandia.gov\/radar\/Web\/images\/SAND2005-3706P-miniSARflight-SAR-images.pdf."},{"key":"ref_27","unstructured":"(2021, October 20). FARADSAR Public Release Data [EB\/OL], Available online: https:\/\/www.sandia.gov\/radar\/complex_data\/FARAD_KA_BAND.zip."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1109\/TITS.2016.2517826","article-title":"Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature","volume":"17","author":"Chen","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the NIPS-W, Long Beach, CA, USA."},{"key":"ref_30","unstructured":"Ayush, E., and Glenn, J. (2021, August 06). yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., and Liu, Z. (2021). End-to-End Semi-Supervised Object Detection with Soft Teacher. arXiv.","DOI":"10.1109\/ICCV48922.2021.00305"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:55:41Z","timestamp":1760169341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,29]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14010143"],"URL":"https:\/\/doi.org\/10.3390\/rs14010143","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,29]]}}}