{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:35:13Z","timestamp":1771065313718,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"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":["61571099"],"award-info":[{"award-number":["61571099"]}],"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>Synthetic aperture radar (SAR) is an important active microwave imaging sensor [...]<\/jats:p>","DOI":"10.3390\/rs15020303","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T02:00:57Z","timestamp":1672884057000},"page":"303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Synthetic Aperture Radar (SAR) Meets Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Tianwen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6780-6100","authenticated-orcid":false,"given":"Tianjiao","family":"Zeng","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Engineering, University of Hong Kong, Hong Kong 999077, China"},{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Xiaoling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1126\/science.204.4400.1405","article-title":"Seasat mission overview","volume":"204","author":"Born","year":"1979","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"De Novellis, V., Castaldo, R., Lollino, P., Manunta, M., and Tizzani, P. (2016). Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys. Remote Sens., 8.","DOI":"10.3390\/rs8080670"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.3390\/rs5063101","article-title":"Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region","volume":"5","author":"Paradella","year":"2013","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.3390\/rs6032393","article-title":"Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan","volume":"6","author":"Khan","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5598","DOI":"10.3390\/rs5115598","article-title":"A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains","volume":"5","author":"Martinis","year":"2013","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhang, X., and Zhang, T. (2022). Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14041018"},{"key":"ref_8","first-page":"4511005","article-title":"A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhang, X., Shao, Z., Shi, J., Wei, S., Zhang, T., and Zeng, T. (2022). A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14205276"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2022). HTC+ for SAR Ship Instance Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14102395"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/MGRS.2022.3145854","article-title":"Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, C., Su, H., Gao, L., and Wang, T. (2022). Deep Learning for SAR Ship Detection: Past, Present and Future. Remote Sens., 14.","DOI":"10.3390\/rs14112712"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xia, R., Chen, J., Huang, Z., Wan, H., Wu, B., Sun, L., Yao, B., Xiang, H., and Xing, M. (2022). CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14061488"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feng, Y., Chen, J., Huang, Z., Wan, H., Xia, R., Wu, B., Sun, L., and Xing, M. (2022). A Lightweight Position-Enhanced Anchor-Free Algorithm for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14081908"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, Z., Gao, R., Huang, K., and Xu, Q. (2022). Triangle Distance IoU Loss, Attention-Weighted Feature Pyramid Network, and Rotated-SARShip Dataset for Arbitrary-Oriented SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14184676"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xiao, X., Li, C., and Lei, Y. (2022). A Lightweight Self-Supervised Representation Learning Algorithm for Scene Classification in Spaceborne SAR and Optical Images. Remote Sens., 14.","DOI":"10.3390\/rs14132956"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ka\u010dan, M., Tur\u010dinovi\u0107, F., Bojanjac, D., and Bosiljevac, M. (2022). Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data. Remote Sens., 14.","DOI":"10.3390\/rs14225673"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bao, J., Zhang, X., Zhang, T., Shi, J., and Wei, S. (2021). A Novel Guided Anchor Siamese Network for Arbitrary Target-of-Interest Tracking in Video-SAR. Remote Sens., 13.","DOI":"10.3390\/rs13224504"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tan, D., Liu, Y., Li, G., Yao, L., Sun, S., and He, Y. (2021). Serial GANs: A Feature-Preserving Heterogeneous Remote Sensing Image Transformation Model. Remote Sens., 13.","DOI":"10.3390\/rs13193968"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, G., Li, Z., Li, X., and Liu, S. (2021). Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images. Remote Sens., 13.","DOI":"10.3390\/rs13214383"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Habibollahi, R., Seydi, S.T., Hasanlou, M., and Mahdianpari, M. (2022). TCD-Net: A Novel Deep Learning Framework for Fully Polarimetric Change Detection Using Transfer Learning. Remote Sens., 14.","DOI":"10.3390\/rs14030438"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fan, Y., Wang, F., and Wang, H. (2022). A Transformer-Based Coarse-to-Fine Wide-Swath SAR Image Registration Method under Weak Texture Conditions. Remote Sens., 14.","DOI":"10.3390\/rs14051175"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, Y., Si, Y., Peng, B., Xiao, F., Luo, S., and He, L. (2022). A Low-Grade Road Extraction Method Using SDG-DenseNet Based on the Fusion of Optical and SAR Images at Decision Level. Remote Sens., 14.","DOI":"10.3390\/rs14122870"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Pu, L., Zhang, X., Zhou, Z., Li, L., Zhou, L., Shi, J., and Wei, S. (2021). A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network. Remote Sens., 13.","DOI":"10.3390\/rs13224564"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:58:58Z","timestamp":1760119138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,4]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020303"],"URL":"https:\/\/doi.org\/10.3390\/rs15020303","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,4]]}}}