{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:42:55Z","timestamp":1774046575086,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field.<\/jats:p>","DOI":"10.3390\/rs16203775","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:10:16Z","timestamp":1728634216000},"page":"3775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Systematic Review of Ship Wake Detection Methods in Satellite Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2320-5070","authenticated-orcid":false,"given":"Andrea","family":"Mazzeo","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, University of Naples \u201cFederico II\u201d, Piazzale Tecchio, 80, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1236-0594","authenticated-orcid":false,"given":"Alfredo","family":"Renga","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples \u201cFederico II\u201d, Piazzale Tecchio, 80, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9260-6736","authenticated-orcid":false,"given":"Maria Daniela","family":"Graziano","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples \u201cFederico II\u201d, Piazzale Tecchio, 80, 80125 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","unstructured":"IMO (2024). AIS Transponders\u2014Regulations for Carriage of AIS, IMO."},{"key":"ref_2","unstructured":"Pichel, G.W., Clemente-Col\u00f3n, P., Wackerman, C.C., and Friedman, K.S. (2004). Chapter 12: Ship and Wake Detection, Synthetic Aperture Radar Marine User\u2019s Manual."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.actaastro.2016.07.001","article-title":"Ship heading and velocity analysis by wake detection in SAR images","volume":"128","author":"Graziano","year":"2016","journal-title":"Acta Astronaut."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Graziano, M., Rufino, G., and D\u2019Errico, M. (2014). Wake-based ship route estimation in high-resolution SAR images. SAR Image Analysis, Modeling, and Techniques XIV, SPIE.","DOI":"10.1117\/12.2067301"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4379","DOI":"10.1109\/JSTARS.2019.2949006","article-title":"Ship Velocity Estimation From Ship Wakes Detected Using Convolutional Neural Networks","volume":"12","author":"Kang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112375","DOI":"10.1016\/j.rse.2021.112375","article-title":"A novel technique for ship wake detection from optical images","volume":"258","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"Arnold, A., Khenchaf, A., and Martin, A. (2006). An evaluation of current ship wake detection algorithms in SAR images. Caract\u00e9risation du Milieu Marin, Brest, France, Citeseer."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113345","DOI":"10.1016\/j.rse.2022.113345","article-title":"Towards real-time detection of ships and wakes with lightweight deep learning model in Gaofen-3 SAR images","volume":"284","author":"Ding","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_9","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv."},{"key":"ref_10","first-page":"1","article-title":"Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-Based Wake Detection via Optical Images","volume":"60","author":"Xue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117075","DOI":"10.1016\/j.oceaneng.2024.117075","article-title":"Kelvin wake detection from large-scale optical imagery using simulated data trained deep neural network","volume":"297","author":"Liu","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, S., and Wang, H. (2022, January 26\u201328). A Review on The Vessel of Hull and Wake Detection for Infrared Remote Sensing Images. Proceedings of the 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), Shanghai, China.","DOI":"10.1109\/MMSP55362.2022.9949007"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.apor.2019.05.006","article-title":"Simulation of Kelvin wakes in optical images of rough sea surface","volume":"89","author":"Liu","year":"2019","journal-title":"Appl. Ocean Res."},{"key":"ref_14","unstructured":"White, F.M. (1999). Fluid Mechanics, WCB\/McGraw-Hill. [4th ed.]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1109\/LGRS.2017.2751083","article-title":"SAR-Based Vessel Velocity Estimation From Partially Imaged Kelvin Pattern","volume":"14","author":"Panico","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1175\/JTECH-D-18-0021.1","article-title":"Ship Wakes in Optical Images","volume":"35","author":"Liu","year":"2018","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.isprsjprs.2022.12.008","article-title":"Comparison of detectability of ship wake components between C-Band and X-Band synthetic aperture radar sensors operating under different slant ranges","volume":"196","author":"Tings","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tings, B., Pleskachevsky, A., Velotto, D., and Jacobsen, S. (2019). Extension of Ship Wake Detectability Model for Non-Linear Influences of Parameters Using Satellite Based X-Band Synthetic Aperture Radar. Remote Sens., 11.","DOI":"10.3390\/rs11050563"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2196159","DOI":"10.1080\/15481603.2023.2196159","article-title":"Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery","volume":"60","author":"Liu","year":"2023","journal-title":"GIScience Remote Sens."},{"key":"ref_20","first-page":"96741F","article-title":"Infrared imaging simulation and detection of ship wake","volume":"Volume 9674","author":"Gong","year":"2015","journal-title":"Proceedings of the AOPC 2015: Optical and Optoelectronic Sensing and Imaging Technology"},{"key":"ref_21","first-page":"1450","article-title":"Infrared characterization and detection of ship wake based on ray tracing method","volume":"44","author":"Zhang","year":"2015","journal-title":"Hongwai Yu Jiguang Gongcheng\/Infrared Laser Eng."},{"key":"ref_22","unstructured":"Iersel, M., and Devecchi, B. (2015, January 21\u201324). Modeling the infrared and radar signature of the wake of a vessel. Proceedings of the SPIE Remote Sensing and Security + Defence, Toulouse, France."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1109\/TGRS.2018.2828833","article-title":"Ship Wake Components: Isolation, Reconstruction, and Characteristics Analysis in Spectral, Spatial, and TerraSAR-X Image Domains","volume":"56","author":"Sun","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, J., Guo, L., Wei, Y., and Chai, S. (2023). Study on Ship Kelvin Wake Detection in Numerically Simulated SAR Images. Remote Sens., 15.","DOI":"10.3390\/rs15041089"},{"key":"ref_25","first-page":"3209006","article-title":"Observation Frequency Analysis for Multiconstellation RadarSystems over the Mediterranean Sea","volume":"2023","author":"Graziano","year":"2023","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/TMI.1986.4307775","article-title":"On the determination of functions from their integral values along certain manifolds","volume":"5","author":"Radon","year":"1986","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","unstructured":"Hough, P.V.C. (1962). A Method and Means for Recognition Complex Patterns. (US3069654A)."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1016\/j.patcog.2007.11.013","article-title":"Old and new straight-line detectors: Description and comparison","volume":"41","author":"Egli","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/36.508418","article-title":"An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions","volume":"34","author":"Eldhuset","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Graziano, M. (2020). Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12182869"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/TGRS.1990.572948","article-title":"Application of Radon Transform Techniques to Wake Detection in Seasat-A SAR Images","volume":"28","author":"Rey","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/36.368224","article-title":"Localized Radon transform-based detection of ship wakes in SAR images","volume":"33","author":"Copeland","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Scherbakov, A., Hanssen, R., Vosselman, G., and Feron, R. (1996, January 23\u201327). Ship wake detection using Radon transforms of filtered SAR imagery. Proceedings of the SPIE\u2014The International Society for Optical Engineering, Taormina, Italy. Microwave Sensing and Synthetic Aperture Radar.","DOI":"10.1117\/12.262684"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/LGRS.2017.2777264","article-title":"Low-Rank Plus Sparse Decomposition and Localized Radon Transform for Ship-Wake Detection in Synthetic Aperture Radar Images","volume":"15","author":"Biondi","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2004.833390","article-title":"The speed and beam of a ship from its wake\u2019s SAR images","volume":"42","author":"Zilman","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Graziano, M.D., D\u2019Errico, M., and Rufino, G. (2016). Wake Component Detection in X-Band SAR Images for Ship Heading and Velocity Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060498"},{"key":"ref_37","unstructured":"Warrick, A., and Delaney, P. (1997, January 21\u201324). Detection of linear features using a localized Radon transform with a wavelet filter. Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1016\/j.sigpro.2005.02.013","article-title":"An improvement of ship wake detection based on the radon transform","volume":"85","author":"Courmontagne","year":"2005","journal-title":"Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Graziano, M., Grasso, M., and D\u2019Errico, M. (2017). Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images. Remote Sens., 9.","DOI":"10.3390\/rs9111107"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/LGRS.2018.2868365","article-title":"A Polarimetric Extension of Low-Rank Plus Sparse Decomposition and Radon Transform for Ship Wake Detection in Synthetic Aperture Radar Images","volume":"16","author":"Biondi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TIP.2005.863021","article-title":"Line detection in images through regularized hough transform","volume":"15","author":"Aggarwal","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/LGRS.2010.2100076","article-title":"A Novel Ship Wake CFAR Detection Algorithm Based on SCR Enhancement and Normalized Hough Transform","volume":"8","author":"Jiaqiu","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Graziano, M.D., and Renga, A. (2021). Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images. Remote Sens., 13.","DOI":"10.3390\/rs13101955"},{"key":"ref_44","unstructured":"Krishnaveni, M., Thakur, S., and Subashini, P. (2009). An Optimal Method For Wake Detection In SAR Images Using Radon Transformation Combined with Wavelet Filters. Int. J. Comput. Sci. Inf. Secur., 6."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/TGRS.2019.2947360","article-title":"Ship Wake Detection in SAR Images via Sparse Regularization","volume":"58","author":"Rizaev","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nan, J., Wang, C., Zhang, B., Wu, F., Zhang, H., and Tang, Y. (2013, January 21\u201326). Ship wake CFAR detection algorithm in SAR images based on length normalized scan. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723599"},{"key":"ref_47","unstructured":"Tings, B., and Velotto, D. (2018, January 4\u20137). Ship Wake Detectability and Classification on TerraSAR-X high resolution data. Proceedings of the EUSAR 2018; 12th European Conference on Synthetic Aperture Radar, Aachen, Germany."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101463","DOI":"10.1016\/j.scs.2019.101463","article-title":"Application of periodic structure scattering in Kelvin ship wakes detection","volume":"47","author":"Wei","year":"2019","journal-title":"Sustain. Cities Soc."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Yang, Z., Li, K., and Liu, T. (2024). Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection. Remote Sens., 16.","DOI":"10.3390\/rs16040658"},{"key":"ref_50","first-page":"313","article-title":"A novel ship wake detection method of SAR images based on frequency domain","volume":"20","author":"Liu","year":"2003","journal-title":"J. Electron."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1007\/s00343-019-8221-y","article-title":"Rapid detection to long ship wake in synthetic aperture radar satellite imagery","volume":"37","author":"Chen","year":"2019","journal-title":"J. Oceanol. Limnol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yang, T., Karaku\u015f, O., and Achim, A. (2020, January 25\u201328). Detection Of Ship Wakes In Sar Imagery Using Cauchy Regularisation. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Virtual.","DOI":"10.1109\/ICIP40778.2020.9190920"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Guan, Y., Xu, H., and Li, C. (2023, January 16\u201321). A Method of Ship Wake Detection in SAR Images Based on Reconstruction Features and Anomaly Detector. Proceedings of the IGARSS 2023\u20142023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10281571"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yang, G., Yu, J., Xiao, C., and Sun, W. (2016, January 20\u201325). Ship wake detection for SAR images with complex backgrounds based on morphological dictionary learning. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472006"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"012101","DOI":"10.1088\/1742-6596\/2033\/1\/012101","article-title":"Application Of Electrical Ship Wakes Detection Trace of Synthetic Aperture Radar (SAR) Image in Coast Guard","volume":"2033","author":"Zhang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ding, K., Yang, J., Wang, Z., Ni, K., Wang, X., and Zhou, Q. (2022). Specific Windows Search for Multi-Ship and Multi-Scale Wake Detection in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14010025"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., and Gao, Y. (2022). Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey. Remote Sens., 14.","DOI":"10.3390\/rs14102385"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s10710-017-9314-z","article-title":"Deep learning","volume":"19","author":"Heaton","year":"2018","journal-title":"Genet. Program. Evolvable Mach."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., and Doll\u00e1r, P. (2015). Microsoft COCO: Common Objects in Context. arXiv.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Del Prete, R., Graziano, M.D., and Renga, A. (2021). First Results on Wake Detection in SAR Images by Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13224573"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, H., Nie, D., Zuo, Y., Tang, L., and Zhang, M. (2022). Nonlinear Ship Wake Detection in SAR Images Based on Electromagnetic Scattering Model and YOLOv5. Remote Sens., 14.","DOI":"10.3390\/rs14225788"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Esposito, C., Prete, R.D., Graziano, M.D., and Renga, A. (2022, January 17\u201322). First Results of Ship Wake Detection by Deep Learning Techniques in Multispectral Spaceborne Images. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883511"},{"key":"ref_64","first-page":"1","article-title":"Keypoints Method for Recognition of Ship Wake Components in Sentinel-2 Images by Deep Learning","volume":"20","author":"Graziano","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_65","first-page":"1","article-title":"OpenSARWake: A Large-Scale SAR Dataset for Ship Wake Recognition with a Feature Refinement Oriented Detector","volume":"21","author":"Xu","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Rizaev, I.G., and Achim, A. (2022). SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea. Remote Sens., 14.","DOI":"10.20944\/preprints202207.0450.v1"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kateb, F.A., Monowar, M.M., Hamid, M.A., Ohi, A.Q., and Mridha, M.F. (2021). FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards. Agronomy, 11.","DOI":"10.3390\/agronomy11122440"},{"key":"ref_68","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_69","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_70","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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_73","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_75","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_76","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_77","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_78","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_79","doi-asserted-by":"crossref","unstructured":"Zhao, T., Wang, Y., Li, Z., Gao, Y., Chen, C., Feng, H., and Zhao, Z. (2024). Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances. Remote Sens., 16.","DOI":"10.3390\/rs16071145"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020). Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection. arXiv.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, C., Luo, Z., Lian, S., and Li, S. (2018, January 20\u201324). Anchor Free Network for Multi-Scale Face Detection. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545814"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2020). CornerNet: Detecting Objects as Paired Keypoints. Int. J. Comput. Vis., 128.","DOI":"10.1007\/s11263-019-01204-1"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., and Kr\u00e4henb\u00fchl, P. (2019). Bottom-up Object Detection by Grouping Extreme and Center Points. arXiv.","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref_84","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2023). Attention Is All You Need. arXiv."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1109\/TGRS.2019.2899955","article-title":"\u211b2-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images","volume":"57","author":"Pang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","article-title":"CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TPAMI.2022.3166956","article-title":"SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing","volume":"45","author":"Yang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_90","unstructured":"Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_91","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_92","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2018). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2019). Squeeze-and-Excitation Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A ConvNet for the 2020s. arXiv.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1002\/gdj3.73","article-title":"A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode","volume":"6","author":"Wang","year":"2019","journal-title":"Geosci. Data J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3775\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:11:19Z","timestamp":1760112679000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,11]]},"references-count":96,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203775"],"URL":"https:\/\/doi.org\/10.3390\/rs16203775","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,11]]}}}