{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:33:21Z","timestamp":1773909201662,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T00:00:00Z","timestamp":1710028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["42027805"],"award-info":[{"award-number":["42027805"]}]},{"name":"National Science Foundation of China","award":["62201124"],"award-info":[{"award-number":["62201124"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clutter interference, and densely arranged ships in complex inshore, have not been well solved so far. Therefore, this article puts forward a novel SAR ship-detection method called multi-level feature-refinement anchor-free framework with a consistent label-assignment mechanism, which is capable of boosting ship-detection performance in complex scenes. First, considering that SAR ship detection is susceptible to complex background interference, we develop a stepwise feature-refinement backbone network to refine the position and contour of the ship object. Next, we devise an adjacent feature-refined pyramid network following the backbone network. The adjacent feature-refined pyramid network consists of the sub-pixel sampling-based adjacent feature-fusion sub-module and adjacent feature-localization enhancement sub-module, which can improve the detection capability of multi-scale objects by mitigating multi-scale high-level semantic loss and enhancing low-level localization features. Finally, to solve the problems of unbalanced positive and negative samples and densely arranged ship detection, we propose a consistent label-assignment mechanism based on consistent feature scale constraints to assign more appropriate and consistent labels to samples. Extensive qualitative and quantitative experiments on three public datasets, i.e., SAR Ship-Detection Dataset (SSDD), High-Resolution SAR Image Dataset (HRSID), and SAR-Ship-Dataset illustrate that the proposed method is superior to many state-of-the-art SAR ship-detection methods.<\/jats:p>","DOI":"10.3390\/rs16060975","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T08:56:41Z","timestamp":1710147401000},"page":"975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multi-Level Feature-Refinement Anchor-Free Framework with Consistent Label-Assignment Mechanism for Ship Detection in SAR Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Yun","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Communication and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Sensen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Communication and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Haohao","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Information Communication and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Junyi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Communication and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Lin","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Information Communication and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Xuegang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Communication and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/7.135446","article-title":"A CFAR adaptive matched filter detector","volume":"28","author":"Robey","year":"1992","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1109\/TSP.2002.800412","article-title":"Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection","volume":"50","author":"Conte","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5260","DOI":"10.1109\/TSP.2014.2348952","article-title":"A CFAR adaptive subspace detector based on a single observation in system-dependent clutter background","volume":"62","author":"Lei","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/LGRS.2016.2618604","article-title":"A modified CFAR algorithm based on object proposals for ship target detection in SAR images","volume":"13","author":"Dai","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","first-page":"806","article-title":"A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images","volume":"10","author":"Qin","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/LGRS.2018.2838263","article-title":"Superpixel-level CFAR detectors for ship detection in SAR imagery","volume":"15","author":"Pappas","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4811","DOI":"10.1109\/TGRS.2017.2701813","article-title":"CFAR ship detection in nonhomogeneous sea clutter using polarimetric SAR data based on the notch filter","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TIP.2019.2938879","article-title":"Me r-cnn: Multi-expert r-cnn for object detection","volume":"29","author":"Lee","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/TIP.2020.3029901","article-title":"Hier R-CNN: Instance-level human parts detection and a new benchmark","volume":"30","author":"Yang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","first-page":"1137","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. Process. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201320). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, N., Ren, H., Deng, T., and Fan, X. (2023). A Lightweight Radar Ship Detection Framework with Hybrid Attentions. Remote Sens., 15.","DOI":"10.3390\/rs15112743"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1109\/TIP.2019.2947806","article-title":"Mask SSD: An effective single-stage approach to object instance segmentation","volume":"29","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_20","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding YOLO series in 2021. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., and Savvides, M. (2019, January 15\u201320). Feature selective anchor-free module for single-shot object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shi, H., Fang, Z., Wang, Y., and Chen, L. (2022). An adaptive sample assignment strategy based on feature enhancement for ship detection in SAR images. Remote Sens., 14.","DOI":"10.3390\/rs14092238"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yao, C., Xie, P., Zhang, L., and Fang, Y. (2022). ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14236058"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TIP.2022.3231126","article-title":"Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery","volume":"32","author":"Wang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1109\/TAES.2023.3237520","article-title":"Global and Local Context-Aware Ship Detector for High-Resolution SAR Images","volume":"59","author":"Wang","year":"2023","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zeng, T., and Zhang, X. (2023). Synthetic aperture radar (SAR) meets deep learning. Remote Sens., 15.","DOI":"10.3390\/books978-3-0365-6383-1"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (2019, January 15\u201320). Centernet: Keypoint triplets for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107787","DOI":"10.1016\/j.patcog.2020.107787","article-title":"A CenterNet++ model for ship detection in SAR images","volume":"112","author":"Guo","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7799","DOI":"10.1109\/JSTARS.2021.3099483","article-title":"An anchor-free detection method for ship targets in high-resolution SAR images","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","first-page":"5219514","article-title":"AFSar: An anchor-free SAR target detection algorithm based on multiscale enhancement representation learning","volume":"60","author":"Wan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"5222212","article-title":"BANet: A balance attention network for anchor-free ship detection in SAR images","volume":"60","author":"Hu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"3218","DOI":"10.1109\/JSTARS.2023.3244616","article-title":"A Survey on Deep-Learning-Based Real-Time SAR Ship Detection","volume":"16","author":"Li","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","first-page":"5217712","article-title":"A robust one-stage detector for multiscale ship detection with complex background in massive SAR images","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., 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_40","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 7\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 8\u201322). Squeeze-and-excitation networks. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"30685","DOI":"10.1007\/s11042-022-11940-1","article-title":"CE-FPN: Enhancing channel information for object detection","volume":"81","author":"Luo","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_44","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_45","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_46","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_47","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., and Su, H. (2021). SAR ship detection dataset (SSDD): Official release and comprehensive data analysis. Remote Sens., 13.","DOI":"10.3390\/rs13183690"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., and Ke, X. (2021). Quad-FPN: A novel quad feature pyramid network for SAR ship detection. Remote Sens., 13.","DOI":"10.3390\/rs13142771"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/6\/975\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:11:40Z","timestamp":1760105500000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/6\/975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,10]]},"references-count":48,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16060975"],"URL":"https:\/\/doi.org\/10.3390\/rs16060975","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,10]]}}}