{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T20:46:20Z","timestamp":1774730780670,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271034"],"award-info":[{"award-number":["62271034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the widespread application and functional complexity of deep neural networks (DNNs), the demand for training samples is increasing. This elevated requirement also extends to DNN-based SAR object detection. Most public SAR object detection datasets are oriented to marine targets such as ships, while data sets oriented to land targets are relatively rare, though they are an effective way to improve the land object detection capability of deep models through SAR sample generation. In this paper, a synthesis generation collaborative SAR sample augmentation framework is proposed to achieve flexible and diverse high-quality sample augmentation. First, a semantic-layout-guided image synthesis strategy is proposed to generate diverse detection samples. The issues of object location rationality and object layout diversity are also addressed. Meanwhile, a pix2pixGAN network guided by layout maps is utilized to achieve diverse background augmentation. Second, a progressive training strategy of diffusion models is proposed to achieve semantically controllable SAR sample generation to further improve the diversity of scene clutter. Finally, a sample cleaning method considering distribution migration and network filtering is employed to further improve the quality of detection samples. The experimental results show that this semantic synthesis generation method can outperform existing sample augmentation methods, leading to a comprehensive improvement in the accuracy metrics of classical detection networks.<\/jats:p>","DOI":"10.3390\/rs15245654","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T03:35:31Z","timestamp":1701920131000},"page":"5654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semantic-Layout-Guided Image Synthesis for High-Quality Synthetic-Aperature Radar Detection Sample Generation"],"prefix":"10.3390","volume":"15","author":[{"given":"Yi","family":"Kuang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4906-6142","authenticated-orcid":false,"given":"Fei","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangfang","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100045, China"},{"name":"Shandong Key Laboratory of Low-Altitude Airspace Surveillance Network Technology, Heze 274201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingbing","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-2373","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,7]]},"reference":[{"key":"ref_1","first-page":"5200817","article-title":"What Catch Your Attention in SAR Images: Saliency Detection Based on Soft-Superpixel Lacunarity Cue","volume":"61","author":"Ma","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"5205516","article-title":"A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery","volume":"61","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/JSTARS.2017.2787728","article-title":"Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","first-page":"652","article-title":"Scattering information and meta-learning based SAR images interpretation for aircraft target recognition","volume":"11","author":"Lyu","year":"2022","journal-title":"J. Radars"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wangiyana, S., Samczy\u0144ski, P., and Gromek, A. (2022). Data augmentation for Building Footprint Segmentation in SAR Images: An Empirical Study. Remote Sens., 14.","DOI":"10.3390\/rs14092012"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6572362","DOI":"10.1155\/2021\/6572362","article-title":"Improving SAR Object Recognition Performance Using Multiple Preprocessing Techniques","volume":"2021","author":"Ma","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, R. (2021). Rethinking the Random Cropping Data augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector. Sensors, 13.","DOI":"10.3390\/rs13010034"},{"key":"ref_8","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jiang, T., Cui, Z., Zhou, Z., and Cao, Z. (2018, January 22\u201327). Data augmentation with Gabor Filter in Deep Convolutional Neural Networks for Sar Object Recognition. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518792"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tang, G., Zhao, H., Claramunt, C., and Men, S. (2022). FLNet: A Near-shore Ship Detection Method Based on Image Enhancement Technology. Remote Sens., 14.","DOI":"10.3390\/rs14194857"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gong, C., Wang, D., Li, M., Chandra, V., and Liu, Q. (2021, January 19\u201321). Keep augment: A simple information-preserving data augmentation approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00111"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5623010","DOI":"10.1109\/TGRS.2022.3173172","article-title":"Unmixing-Based Spatiotemporal Image Fusion Accounting for Complex Land Cover Changes","volume":"60","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Jayaram, V., Curless, B., Seitz, S.M., and Kemelmacher-Shlizerman, I. (2020, January 13\u201319). Background Matting: The World Is Your Green Screen. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00236"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8093","DOI":"10.1049\/joe.2019.0696","article-title":"SAR image synthesis based on conditional generative adversarial networks","volume":"2019","author":"Wang","year":"2019","journal-title":"J. Eng."},{"key":"ref_15","first-page":"637","article-title":"Multiangle SAR dataset construction of aircraft targets based on angle interpolation simulation","volume":"11","author":"Wang","year":"2022","journal-title":"J. Radars"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Geng, Z., Xu, Y., Wang, B.-N., Yu, X., Zhu, D.Y., and Zhang, G. (2023). Object Recognition in SAR Images by Deep Learning with Training Data augmentation. Sensors, 23.","DOI":"10.3390\/s23020941"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8586","DOI":"10.1109\/JSTARS.2022.3208928","article-title":"Detecting Fine-Grained Airplanes in SAR Images with Sparse Attention-Guided Pyramid and Class-Balanced Data augmentation","volume":"15","author":"Bao","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Suo, Z., Zhao, Y., Chen, S., and Hu, Y. (2022). BoxPaste: An Effective Data augmentation Method for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14225761"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"25459","DOI":"10.1109\/ACCESS.2019.2900522","article-title":"Data augmentation Based on Attributed Scattering Centers to Train Robust CNN for SAR ATR","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/LGRS.2017.2692386","article-title":"Data augmentation by Multilevel Reconstruction Using Attributed Scattering Center for SAR Object Recognition","volume":"14","author":"Ding","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","unstructured":"Inoue, H. (2018). Data augmentation by pairing samples for images classification. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/JSTARS.2023.3239633","article-title":"Attribute-Guided Generative Adversarial Network with Improved Episode Training Strategy for Few-Shot SAR Image Generation","volume":"16","author":"Sun","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9381","DOI":"10.1109\/JSTARS.2022.3218369","article-title":"SAR Object Image Generation Method Using Azimuth-Controllable Generative Adversarial Network","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4701312","DOI":"10.1109\/TGRS.2021.3068532","article-title":"Synthesizing Optical and SAR Imagery from Land Cover Maps and Auxiliary Raster Data","volume":"60","author":"Baier","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108041","DOI":"10.1016\/j.compeleceng.2022.108041","article-title":"Generative knowledge transfer for ship detection in SAR images","volume":"101","author":"Lou","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_26","unstructured":"Huang, H., Zhang, F., Zhou, Y., Yin, Q., and Hu, W. (August, January 28). High Resolution SAR Image Synthesis with Hierarchical Generative Adversarial Networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_27","unstructured":"Serr\u00e0, J., \u00c1lvarez, D., G\u00f3mez, V., Slizovskaia, O., N\u00fa\u00f1ez, J.F., and Luque, J. (2019). Input complexity and out-of-distribution detection with likelihood-based generative models. arXiv."},{"key":"ref_28","unstructured":"Mohamed, S., and Lakshminarayanan, B. (2016). Learning in implicit generative models. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chung, Y.A., Hsu, W.N., Tang, H., and Glass, J. (2019). An unsupervised autoregressive model for speech representation learning. arXiv.","DOI":"10.21437\/Interspeech.2019-1473"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Boutsidis, C., Woodruff, D.P., and Zhong, P. (2016, January 19\u201321). Optimal principal component analysis in distributed and streaming models. Proceedings of the 48th Annual ACM Symposium on Theory of Computing, Cambridge, MA, USA.","DOI":"10.1145\/2897518.2897646"},{"key":"ref_31","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_32","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial networks. arXiv."},{"key":"ref_33","unstructured":"Niu, L., Cong, W., Liu, L., Hong, Y., Zhang, B., Liang, J., and Zhang, L. (2021). Making Images Real Again: A Comprehensive Survey on Deep Image Composition. arXiv."},{"key":"ref_34","first-page":"497","article-title":"A survey on image matting methods based on deep learning","volume":"3","author":"Fu","year":"2022","journal-title":"Front. Econ. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, N., Price, B., Cohen, S., and Huang, T. (2017, January 21\u201326). Deep image matting. Proceedings of the EEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.41"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cong, W., Zhang, J., Niu, L., Liu, L., Ling, Z., Li, W., and Zhang, L. (2020, January 13\u201319). DoveNet: Deep image harmonization via domain verification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00842"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4759","DOI":"10.1109\/TIP.2020.2975979","article-title":"Improving the harmony of the composite image by spatial-separated attention module","volume":"29","author":"Cun","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1007\/s11263-020-01336-9","article-title":"Compositional GAN: Learning image conditional binary composition","volume":"128","author":"Azadi","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wen, T., Min, J., Wang, J., Han, D., and Shi, J. (2020, January 23\u201328). Learning object placement by inpainting for compositional data augmentation. Proceedings of the Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58601-0_34"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Katsumata, K., and Nakayama, H. (2021, January 6\u201311). Semantic image synthesis from inaccurate and coarse masks. Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414521"},{"key":"ref_41","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lefevre, S. (2018, January 22\u201327). Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518321"},{"key":"ref_43","unstructured":"Wang, G., Dong, G., Li, H., Han, L., Tao, X., and Ren, P. (August, January 28). Remote Sensing Image Synthesis via Graphical Generative Adversarial Networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., and Zhou, T. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_45","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 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_46","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015, January 6\u201311). Deep unsupervised learning using nonequilibrium thermodynamics. Proceedings of the International Conference on Machine Learning 2015, Lille, France."},{"key":"ref_47","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","unstructured":"Song, J., Meng, C., and Ermon, S. (2020). Denoising diffusion implicit models. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5615012","DOI":"10.1109\/TGRS.2023.3268331","article-title":"Efficient and Controllable Remote Sensing Fake Sample Generation Based on Diffusion Model","volume":"61","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/24\/5654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:34:33Z","timestamp":1760132073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/24\/5654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"references-count":49,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15245654"],"URL":"https:\/\/doi.org\/10.3390\/rs15245654","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,7]]}}}