{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T14:21:10Z","timestamp":1785853270656,"version":"3.56.0"},"reference-count":49,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"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":["61991421"],"award-info":[{"award-number":["61991421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62022082"],"award-info":[{"award-number":["62022082"]}],"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>Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. The dataset contains six categories of ships. In total, 30 panoramic SAR tiles of the Chinese Gaofen-3 of port areas with a 1-m resolution were cropped to slices, each with 1024 \u00d7 1024 pixels. In addition, most of the images in the dataset contain nearshore areas with complex background interference. Eight state-of-the-art rotated detectors and a CFAR-based method were used to evaluate the dataset. Experimental results revealed that the complex background will have a great impact on the performance of detectors.<\/jats:p>","DOI":"10.3390\/rs13245104","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"5104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["SRSDD-v1.0: A High-Resolution SAR Rotation Ship Detection Dataset"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7267-7190","authenticated-orcid":false,"given":"Songlin","family":"Lei","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5965-4779","authenticated-orcid":false,"given":"Dongdong","family":"Lu","sequence":"additional","affiliation":[{"name":"Laboratory of Spatial Information Intelligent Processing System, Suzhou Aerospace Information Research Institute, Suzhou 215000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8517-3415","authenticated-orcid":false,"given":"Xiaolan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chibiao","family":"Ding","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"National Key Laboratory of Microwave Imaging Technology, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.033","article-title":"Vessel detection and classification from spaceborne optical images: A literature survey","volume":"207","author":"Kanjir","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liu, L., Qiu, X., and Lei, B. (2017). Fast vessel detection in Gaofen-3 SAR images with ultrafine strip-map mode. Sensors, 17.","DOI":"10.3390\/s17071578"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/LGRS.2016.2539218","article-title":"Cfar line detector for polarimetric SAR images using wilks\u2019 test statistic","volume":"13","author":"Jin","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4884","DOI":"10.1109\/JSTARS.2018.2879082","article-title":"SAR target CFAR detection via gpu parallel operation","volume":"11","author":"Cui","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_9","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv, Available online: https:\/\/arxiv.org\/abs\/1804.02767."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kang, M., Leng, X., Lin, Z., and Ji, K. (2017, January 18\u201321). A modified faster R-CNN based on CFAR algorithm for SAR ship detection. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., and Lin, Z. (2017). Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection. Remote Sens., 9.","DOI":"10.3390\/rs9080860"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, M.H., Xu, P., and Guo, Z.W. (2017, January 18\u201321). SAR ship detection using sea-land segmentation-based convolutional neural network. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958806"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T., and Le, Q.V. (2019, January 16\u201320). NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2019). EfficientDet: Scalable and Efficient Object Detection. arXiv.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"141662","DOI":"10.1109\/ACCESS.2019.2943241","article-title":"A Lightweight Feature Optimizing Network for Ship Detection in SAR Image","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104848","DOI":"10.1109\/ACCESS.2019.2930939","article-title":"A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","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":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/LGRS.2020.2993899","article-title":"ShipDeNet-20: An Only 20 Convolution Layers and <1-MB Lightweight SAR Ship Detector","volume":"18","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/JSTARS.2020.3041783","article-title":"Learning Slimming SAR Ship Object Detector through Network Pruning and Knowledge Distillation","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","first-page":"852","article-title":"AIR-SARShip-1.0: High-resolution SAR ship detection dataset","volume":"8","author":"Sun","year":"2019","journal-title":"J. Radars"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-Oriented Scene Text Detection via Rotation Proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., and Liang, J. (2017, January 21\u201326). EAST: An Efficient and Accurate Scene Text Detector. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.283"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., and Luo, Z. (2017). R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection. arXiv.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). Dota: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, C., and Jiang, W. (2018). Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression. Sensors, 18.","DOI":"10.3390\/s18092851"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8333","DOI":"10.1109\/TGRS.2019.2920534","article-title":"DRBox-v2: An improved detector with rotatable boxes for target detection in SAR images","volume":"57","author":"An","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"159262","DOI":"10.1109\/ACCESS.2019.2951030","article-title":"MSARN: A deep neural network based on an adaptive recalibration mechanism for multiscale and arbitrary-oriented SAR ship detection","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, J., and Zhan, R. (2020). R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12122031"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sens., 11.","DOI":"10.3390\/rs11070765"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Ke, X., Zhan, X., Shi, J., Wei, S., Pan, D., Li, J., Su, H., and Zhou, Y. (2020). LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12182997"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The PASCAL Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u2019ar, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_39","unstructured":"(2020, October 27). Rotated-RetinaNet. Available online: https:\/\/github.com\/ming71\/Rotated-RetinaNet."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G., and Lu, Q. (2019, January 16\u201320). Learning RoI Transformer for Oriented Object Detection in Aerial Images. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_41","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2019). R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_44","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 5\u20139). Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection","volume":"43","author":"Xu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., and Wang, J. (2021). Oriented R-CNN for Object Detection. arXiv.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1109\/JSTARS.2016.2551730","article-title":"A Comparative Study of Operational Vessel Detectors for Maritime Surveillance Using Satellite-Borne Synthetic Aperture Radar","volume":"9","author":"Stasolla","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zheng, R., and Qiu, X. (2021). Fast detection method for low false alarm of multi-channel spaceborne SAR image combined with confidence calculation. J. Remote Sens.","DOI":"10.11834\/jrs.20219336"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:49:01Z","timestamp":1760168941000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":49,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245104"],"URL":"https:\/\/doi.org\/10.3390\/rs13245104","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}