{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:34:51Z","timestamp":1776101691308,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019082","name":"Shanghai Aerospace Science and Technology Innovation Foundation","doi-asserted-by":"publisher","award":["SAST2022-042"],"award-info":[{"award-number":["SAST2022-042"]}],"id":[{"id":"10.13039\/501100019082","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Abundant datasets are critical to train models based on deep learning technologies for ship detection applications. Compared with optical images, ship detection based on synthetic aperture radar (SAR) (especially the high-Earth-orbit spaceborne SAR launched recently) lacks enough training samples. A novel cross-domain attention GAN (CDA-GAN) model is proposed for optical-to-SAR translation, which can generate high-quality SAR amplitude training samples of a target by optical image conversion. This high quality includes high geometry structure similarity of the target compared with the corresponding optical image and low background noise around the target. In the proposed model, the cross-domain attention mechanism and cross-domain multi-scale feature fusion are designed to improve the quality of samples for detection based on the generative adversarial network (GAN). Specifically, a cross-domain attention mechanism is designed to simultaneously emphasize discriminative features from optical images and SAR images at the same time. Moreover, a designed cross-domain multi-scale feature fusion module further emphasizes the geometric information and semantic information of the target in a feature graph from the perspective of global features. Finally, a reference loss is introduced in CDA-GAN to completely retain the extra features generated by the cross-domain attention mechanism and cross-domain multi-scale feature fusion module. Experimental results demonstrate that the training samples generated by the proposed CDA-GAN can obtain higher ship detection accuracy using real SAR data than the other state-of-the-art methods. The proposed method is generally available for different orbit SARs and can be extended to the high-Earth-orbit spaceborne SAR case.<\/jats:p>","DOI":"10.3390\/rs16163001","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:29:57Z","timestamp":1723782597000},"page":"3001","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1644-0716","authenticated-orcid":false,"given":"Baolong","family":"Wu","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Haonan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8064-4342","authenticated-orcid":false,"given":"Cunle","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-9336","authenticated-orcid":false,"given":"Jianlai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Full-Aperture Processing of Airborne Microwave Photonic SAR Raw Data","volume":"61","author":"Chen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5712","DOI":"10.1109\/JSTARS.2024.3370733","article-title":"A Method for Calculating the Optimal Velocity Search Step Size for Airborne Three-Channel SAR Adaptive Clutter Suppression","volume":"17","author":"Li","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","first-page":"4000905","article-title":"An Improved Omega-K Algorithm for Squinted SAR With Curved Trajectory","volume":"21","author":"Li","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1109\/TMM.2019.2907052","article-title":"Quality-Aware Unpaired Image-to-Image Translation","volume":"21","author":"Chen","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/TPAMI.2019.2950198","article-title":"Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation","volume":"43","author":"Lin","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TGRS.2019.2947634","article-title":"What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs","volume":"58","author":"Huang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving into High Quality Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_8","unstructured":"Marmanis, D., Yao, W., Adam, F., Datcu, M., Reinartz, P., Schindler, K., Wegner, J.D., and Stilla, U. (2017, January 21\u201326). Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data. Proceedings of the 2017 Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_9","first-page":"4501005","article-title":"Dynamic Graph-Level Neural Network for SAR Image Change Detection","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10174","DOI":"10.1109\/JSTARS.2021.3113163","article-title":"A Multi-Cooperative Deep Convolutional Neural Network for Spatiotemporal Satellite Image Fusion","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4516305","DOI":"10.1109\/LGRS.2022.3223353","article-title":"A Semi-Supervised Image-to-Image Translation Framework for SAR\u2013Optical Image Matching","volume":"19","author":"Du","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8916","DOI":"10.1109\/TIP.2020.3021789","article-title":"Unified Generative Adversarial Networks for Controllable Image-to-Image Translation","volume":"29","author":"Tang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","first-page":"5233812","article-title":"SAR-to-Optical Image Translation with Hierarchical Latent Features","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1109\/TNNLS.2019.2935384","article-title":"Unsupervised Domain Adaptation With Adversarial Residual Transform Networks","volume":"31","author":"Cai","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"132594","DOI":"10.1109\/ACCESS.2019.2941272","article-title":"ResAttr-GAN: Unpaired Deep Residual Attributes Learning for Multi-Domain Face Image Translation","volume":"7","author":"Tao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, X., Ming-Yu, L., Belongie, S., and Kautz, J. (2018, January 8\u201314). Multimodal Unsupervised Image-to-Image Translation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chaabane, F., R\u00e9jichi, S., and Tupin, F. (2021, January 11\u201316). Self-Attention Generative Adversarial Networks for Times Series VHR Multispectral Image Generation. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553597"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fu, H., Gong, M., Wang, C., Batmanghelich, K., Zhang, K., and Tao, D. (2019, January 15\u201320). Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00253"},{"key":"ref_20","unstructured":"Hsin-Ying, L., Hung-Yu, T., Jia-Bin, H., Singh, M.K., and Yang, M.H. (2018, January 8\u201314). Diverse Image-to-Image Translation via Disentangled Representations. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany."},{"key":"ref_21","unstructured":"Ming-Yu, L., and Tuzel, O. (2016, January 5\u201310). Coupled Generative Adversarial Networks. Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2019.08.022","article-title":"Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving","volume":"367","author":"Shan","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_23","unstructured":"Zhang, R., Pfister, T., and Li, J. (2019). Harmonic Unpaired Image-to-image Translation. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/JSTSP.2020.2994523","article-title":"GAN-Generated Image Detection with Self-Attention Mechanism Against GAN Generator Defect","volume":"14","author":"Mi","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/TMM.2021.3134157","article-title":"FDA-GAN: Flow-Based Dual Attention GAN for Human Pose Transfer","volume":"25","author":"Ma","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_26","unstructured":"Ho, J., Jain, A., and Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. arXiv."},{"key":"ref_27","unstructured":"Sasaki, H., Willcocks, C., and Breckon, T. (2021). UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1109\/TGRS.2017.2656380","article-title":"Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/LGRS.2017.2660067","article-title":"Robust Optical-to-SAR Image Matching Based on Shape Properties","volume":"14","author":"Ye","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5501704","DOI":"10.1109\/LSENS.2020.3041585","article-title":"PFAF-Net: Pyramid Feature Network for Multimodal Fusion","volume":"4","author":"Raza","year":"2020","journal-title":"IEEE Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5224318","DOI":"10.1109\/TGRS.2022.3152854","article-title":"DFAF-Net: A Dual-Frequency PolSAR Image Classification Network Based on Frequency-Aware Attention and Adaptive Feature Fusion","volume":"60","author":"Cao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"6004305","article-title":"Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1109\/JSTARS.2023.3342986","article-title":"Multiscale Complex-Valued Feature Attention Convolutional Neural Network for SAR Automatic Target Recognition","volume":"17","author":"Zhou","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4410118","DOI":"10.1109\/TGRS.2023.3327109","article-title":"CNN-Improved Superpixel-to-Pixel Fuzzy Graph Convolution Network for PolSAR Image Classification","volume":"61","author":"Shi","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhang, M., Wang, H., and Tan, J. (2022). Ship Detection in SAR Images Based on Multi-Scale Feature Extraction and Adaptive Feature Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14030755"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11741","DOI":"10.1109\/JSTARS.2024.3411032","article-title":"CERMF-Net: A SAR-Optical Feature Fusion for Cloud Elimination From Sentinel-2 Imagery Using Residual Multiscale Dilated Network","volume":"17","author":"Anandakrishnan","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shi, J., Nie, M., Ji, S., Shi, C., Liu, H., and Jin, H. (2023). Polarimetric Synthetic Aperture Radar Image Classification Based on Double-Channel Convolution Network and Edge-Preserving Markov Random Field. Remote Sens., 15.","DOI":"10.20944\/preprints202310.0166.v1"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TMM.2020.2975961","article-title":"SPA-GAN: Spatial Attention GAN for Image-to-Image Translation","volume":"23","author":"Emami","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4482","DOI":"10.1109\/JSTARS.2022.3157749","article-title":"SAR Object Detection Encounters Deformed Complex Scenes and Aliased Scattered Power Distribution","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3001\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:37:19Z","timestamp":1760110639000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,15]]},"references-count":39,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16163001"],"URL":"https:\/\/doi.org\/10.3390\/rs16163001","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,15]]}}}