{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:31:10Z","timestamp":1775176270488,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012245","name":"Guangdong Science and Technology Program","doi-asserted-by":"publisher","award":["2021A1515011854"],"award-info":[{"award-number":["2021A1515011854"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Guangdong Science and Technology Program","doi-asserted-by":"publisher","award":["2022A1515011707"],"award-info":[{"award-number":["2022A1515011707"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model\u2019s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models.<\/jats:p>","DOI":"10.3390\/rs15112855","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:27:30Z","timestamp":1685500050000},"page":"2855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["PPA-Net: Pyramid Pooling Attention Network for Multi-Scale Ship Detection in SAR Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8706-4431","authenticated-orcid":false,"given":"Gang","family":"Tang","sequence":"first","affiliation":[{"name":"Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3999-5482","authenticated-orcid":false,"given":"Hongren","family":"Zhao","sequence":"additional","affiliation":[{"name":"Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5586-1997","authenticated-orcid":false,"given":"Christophe","family":"Claramunt","sequence":"additional","affiliation":[{"name":"Naval Academy, Brest Naval, Lanveoc-Poulmic, BP 600, F-29240 Brest, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2707-2533","authenticated-orcid":false,"given":"Weidong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"}]},{"given":"Shiming","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Marine Renewable Energy, Shanghai Ocean University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1461-2003","authenticated-orcid":false,"given":"Yide","family":"Wang","sequence":"additional","affiliation":[{"name":"Institut d\u2019Electronique et Des Technologies du NumeRique (IETR), CNRS UMR6164, Nantes Universit\u00e9, F-44000 Nantes, France"}]},{"given":"Yuehua","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.2174\/1573405617666210218100641","article-title":"A Simplified Framework for the Detection of Intracranial Hemorrhage in CT Brain Images Using Deep Learning","volume":"17","author":"Kumaravel","year":"2021","journal-title":"Curr. Med. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110162","DOI":"10.1016\/j.oceaneng.2021.110162","article-title":"The impact of shipping 4.0 on controlling shipping accidents: A systematic literature review","volume":"243","author":"Sepehri","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Elmi, Z., Singh, P., Meriga, V.K., Goniewicz, K., Borowska-Stefa\u0144ska, M., Wi\u015bniewski, S., and Dulebenets, M.A. (2022). Uncertainties in liner shipping and ship schedule recovery: A state-of-the-art review. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10050563"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111255","DOI":"10.1016\/j.rse.2019.111255","article-title":"The legacy of the SIR-C\/X-SAR radar system: 25 years on","volume":"231","author":"Freeman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MGRS.2021.3065811","article-title":"Artificial intelligence in interferometric synthetic aperture radar phase unwrapping: A review","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112907","DOI":"10.1016\/j.oceaneng.2022.112907","article-title":"Machine learning in sustainable ship design and operation: A review","volume":"266","author":"Huang","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/MGRS.2022.3186904","article-title":"Compact polarimetric synthetic aperture radar for target detection: A review","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1109\/LGRS.2020.3005197","article-title":"A curvature-based saliency method for ship detection in SAR images","volume":"18","author":"Yang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2022.06.006","article-title":"A novel full-polarization SAR image ship detector based on scattering mechanisms and wave polarization anisotropy","volume":"190","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/LGRS.2019.2913873","article-title":"Fast and automatic ship detection for SAR imagery based on multiscale contrast measure","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","first-page":"910","article-title":"Non-Gaussian CFAR techniques for target detection in high resolution SAR images","volume":"1","author":"Kuttikkad","year":"1994","journal-title":"Proc. ICIP"},{"key":"ref_13","unstructured":"Hofele, F.X. (2001, January 15\u201318). An innovative CFAR algorithm. Proceedings of the 2001 CIE International Conference on Radar, Beijing, China."},{"key":"ref_14","unstructured":"Novak, L.M., and Hesse, S.R. (1991, January 4\u20136). On the performance of order-statistics CFAR detectors. Proceedings of the IEEE 25th Asilomar Conference on Signals, Systems & Computer, Pacific Grove, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/TGRS.2004.843190","article-title":"CFAR detection of extended objects in high-resolution SAR images","volume":"43","author":"Galdi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, M., Chen, S., Lu, F., Xing, M., and Wei, J. (2021). Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion. Sensors, 21.","DOI":"10.3390\/s21051643"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/LGRS.2018.2873637","article-title":"Superpixel-based LCM detector for faint ships hidden in strong noise background SAR imagery","volume":"16","author":"Wang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1109\/LGRS.2018.2805714","article-title":"Target detection by exploiting superpixel-level statistical dissimilarity for SAR imagery","volume":"15","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/JSTARS.2021.3049851","article-title":"A Novel CNN-Based Detector for Ship Detection Based on Rotatable Bounding Box in SAR Images","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6201","DOI":"10.1007\/s10462-021-09974-2","article-title":"Benchmarking lightweight face architectures on specific face recognition scenarios","volume":"54","author":"Luevano","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ins.2020.06.060","article-title":"FaultFace: Deep convolutional generative adversarial network (DCGAN) based ball-bearing failure detection method","volume":"542","author":"Viola","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6172","DOI":"10.1109\/TVT.2021.3078482","article-title":"Deep Learning Enhanced Driving Behavior Evaluation Based on Vehicle-Edge-Cloud Architecture","volume":"70","author":"Xun","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tang, G., Zhuge, Y., Claramunt, C., Wang, Y., and Men, S. (2021). N-Yolo: A SAR ship detection using noise-classifying and complete-target extraction. Remote Sens., 13.","DOI":"10.3390\/rs13050871"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2019.03.082","article-title":"AU R-CNN: Encoding expert prior knowledge into R-CNN for action unit detection","volume":"355","author":"Ma","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 1\u201318). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1007\/s10489-020-01665-9","article-title":"Local keypoint-based Faster R-CNN","volume":"50","author":"Ding","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_28","first-page":"5763476","article-title":"Multiobject Detection Algorithm Based on Adaptive Default Box Mechanism","volume":"2020","author":"Li","year":"2020","journal-title":"Complexity"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yoshida, T., and Ouchi, K. (2022). Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method. Remote Sens., 14.","DOI":"10.3390\/rs14194691"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tang, G., Liu, S., Fujino, I., Claramunt, C., Wang, Y., and Men, S. (2020). H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network. Remote Sens., 12.","DOI":"10.3390\/rs12244192"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TII.2020.3030620","article-title":"Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector","volume":"17","author":"Shi","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, DC, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_35","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_36","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., and Feng, J. (November, January 27). Panet: Few-shot image semantic segmentation with prototype alignment. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_37","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao HY, M. (2020, January 13\u201319). YOLOv4: Optimal speed and a acuracy of object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., and Sun, J. (2018). Detnet: A backbone network for object detection. arXiv.","DOI":"10.1007\/978-3-030-01240-3_21"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, C., and Li, W. (2018). Ship classification in high-resolution SAR images via transfer learning with small training dataset. Sensors, 19.","DOI":"10.3390\/s19010063"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep transfer learning for few-shot SAR image classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, X., Meng, G., Guo, C., and Jiang, Z. (2022). Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector. Remote Sens., 14.","DOI":"10.3390\/rs14122790"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Truong, T.N., Do Ngoc, T., Quang, B.N., and Le Tran, S. (2019, January 14\u201316). Combining Multi-Threshold Saliency with Transfer Learning for Ship Detection and Information Extraction from Optical Satellite Images. Proceedings of the 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Dalian, China.","DOI":"10.1109\/ISKE47853.2019.9170323"},{"key":"ref_44","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_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","first-page":"6623","DOI":"10.1109\/TGRS.2020.2978268","article-title":"A patch-to-pixel convolutional neural network for small ship detection with PolSAR images","volume":"58","author":"Jin","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10808","DOI":"10.1109\/JSTARS.2021.3120009","article-title":"Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images","volume":"14","author":"Ma","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2020.05.016","article-title":"HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery","volume":"167","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TGRS.2020.2997200","article-title":"Ship detection in large-scale SAR images via spatial shuffle-group enhance attention","volume":"59","author":"Cui","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"7147","DOI":"10.1109\/TGRS.2018.2848901","article-title":"HSF-Net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230829","article-title":"Multiscale Ship Detection Method in SAR Images Based on Information Compensation and Feature Enhancement","volume":"60","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","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"},{"key":"ref_55","first-page":"1","article-title":"A High-Effective Implementation of Ship Detector for SAR Images","volume":"19","author":"Gao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:45:49Z","timestamp":1760125549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15112855"],"URL":"https:\/\/doi.org\/10.3390\/rs15112855","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}