{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T03:48:33Z","timestamp":1769312913211,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T00:00:00Z","timestamp":1636848000000},"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>Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. Even if deep learning (DL) has led to an impressive performance boost on a variety of computer vision tasks, its usage for automatic target recognition (ATR) in SAR images to support MDA is still limited to the detection of ships rather than ship wakes. A dataset is presented in this paper and several state-of-the-art object detectors based on convolutional neural networks (CNNs) are tested with different backbones. The dataset, including more than 250 wake chips, is realized by visually inspecting Sentinel-1 images over highly trafficked maritime sites. Extensive experiments are shown to characterize CNNs for the wake detection task. For the first time, a deep-learning approach is implemented to specifically detect ship wakes without any a-priori knowledge or cuing about the location of the vessel that generated the wake. No annotated dataset was available to train deep-learning detectors on this task, which is instead presented in this paper. Moreover, the benchmarks achieved for different detectors point out promising features and weak points of the relevant approaches. Thus, the work also aims at stimulating more research in this promising, but still under-investigated, field.<\/jats:p>","DOI":"10.3390\/rs13224573","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["First Results on Wake Detection in SAR Images by Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0810-4050","authenticated-orcid":false,"given":"Roberto","family":"Del Prete","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, 80125 Napoli, 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 Federico II, 80125 Napoli, 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 Federico II, 80125 Napoli, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,14]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"The International Maritime Organization and Maritime Security","volume":"30","author":"Balkin","year":"2006","journal-title":"Tul. Mar. LJ"},{"key":"ref_2","unstructured":"Tetreault, B.J. (2005, January 17\u201323). Use of the Automatic Identification System (AIS) for Maritime Domain wareness (MDA). Proceedings of the Oceans 2005 Mts\/IEEE, Washington, DC, USA."},{"key":"ref_3","unstructured":"Iceye (2021, May 19). Dark Vessel Detection for Maritime Security with SAR Data. Available online: https:\/\/www.iceye.com\/use-cases\/security\/dark-vessel-detection-for-maritime-security."},{"key":"ref_4","unstructured":"exactEarth (2021, May 19). exactEarth | AIS Vessel Tracking | Maritime Ship Monitoring | Home. Available online: https:\/\/www.exactearth.com\/."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Graziano, M.D., Renga, A., and Moccia, A. (2019). Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness. Remote Sens., 11.","DOI":"10.3390\/rs11192196"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., and Shao, J. (2017). Ship Detection in SAR Images Based on an Improved Faster R-CNN. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), IEEE.","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2017.2776357","article-title":"SAR automatic target recognition based on multiview deep learning framework","volume":"56","author":"Pei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, P., Liu, K., Zou, H., and Zhen, X. (2018). Multi-stream convolutional neural network for SAR automatic target recognition. Remote Sens., 10.","DOI":"10.3390\/rs10091473"},{"key":"ref_9","unstructured":"Potin, P., Rosich, B., Miranda, N., and Grimont, P. (2018, January 2\u20136). Sentinel-1a\/-1b mission status. Proceedings of the 12th European Conference on Synthetic Aperture Radar\u2014 VDE (EUSAR 2018), Berlin, Germany."},{"key":"ref_10","first-page":"5828","article-title":"On Solving SAR Imaging Inverse Problems Using Nonconvex Regularization with a Cauchy-Based Penalty","volume":"59","author":"Achim","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"4819","DOI":"10.1080\/01431161.2010.485147","article-title":"Identification of ocean oil spills in SAR imagery based on fuzzy logic algorithm","volume":"31","author":"Liu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17792","DOI":"10.1109\/ACCESS.2020.2965173","article-title":"A two-component deep learning network for SAR image denoising","volume":"8","author":"Gu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., and Zhu, X.X. (2018). The SEN1-2 dataset for deep learning in SAR-optical data fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-141-2018"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cha, M., Majumdar, A., Kung, H., and Barber, J. (2018, January 15\u201320). Improving SAR automatic target recognition using simulated images under deep residual refinements. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462109"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Chang, Y.L., Anagaw, A., Chang, L., Wang, Y.C., Hsiao, C.Y., and Lee, W.H. (2019). Ship detection based on YOLOv2 for SAR imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070786"},{"key":"ref_18","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_19","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv."},{"key":"ref_20","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_24","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2021, May 19). Deep Residual Learning for Image Recognition, Available online: http:\/\/xxx.lanl.gov\/abs\/1512.03385."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_26","unstructured":"Hoiem, D., Divvala, S.K., and Hays, J.H. (2021, May 19). Pascal VOC 2008 Challenge. World Literature Today, Available online: http:\/\/host.robots.ox.ac.uk\/pascal\/VOC\/voc2008\/index.html."},{"key":"ref_27","unstructured":"Liu, Y.C., Ma, C.Y., He, Z., Kuo, C.W., Chen, K., Zhang, P., Wu, B., Kira, Z., and Vajda, P. (2021). Unbiased teacher for semi-supervised object detection. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., Bai, X., and Liu, Z. (2021). End-to-End Semi-Supervised Object Detection with Soft Teacher. arXiv.","DOI":"10.1109\/ICCV48922.2021.00305"},{"key":"ref_29","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_30","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_31","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_32","unstructured":"Beal, J., Kim, E., Tzeng, E., Park, D.H., Zhai, A., and Kislyuk, D. (2020). Toward transformer-based object detection. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., and Shah, M. (2021). Transformers in vision: A survey. arXiv.","DOI":"10.1145\/3505244"},{"key":"ref_35","first-page":"1603","article-title":"Gated softmax classification","volume":"23","author":"Memisevic","year":"2010","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2021, May 19). Mask R-CNN, Available online: http:\/\/xxx.lanl.gov\/abs\/1703.06870."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Han, J., and Moraga, C. (1995). The influence of the sigmoid function parameters on the speed of backpropagation learning. International Workshop on Artificial Neural Networks, Springer.","DOI":"10.1007\/3-540-59497-3_175"},{"key":"ref_38","unstructured":"Cai, Z., and Vasconcelos, N. (2021, May 19). Cascade R-CNN: High Quality Object Detection and Instance Segmentation, Available online: http:\/\/xxx.lanl.gov\/abs\/1906.09756."},{"key":"ref_39","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_40","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2021, May 19). Feature Pyramid Networks for Object Detection, Available online: http:\/\/xxx.lanl.gov\/abs\/1612.03144."},{"key":"ref_41","unstructured":"Pichel, W.G., Clemente-col\u00f3n, P., Wackerman, C.C., and Friedman, K.S. (2004). Ship and Wake Detection. SAR Marine Users Manual, NOAA. Available online: https:\/\/www.sarusersmanual.com\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"R3","DOI":"10.1017\/jfm.2013.607","article-title":"Kelvin wake pattern at large Froude numbers","volume":"738","author":"Darmon","year":"2014","journal-title":"J. Fluid Mech."},{"key":"ref_43","unstructured":"Tunaley, J.K. (2014). Wakes from Go-Fast and Small Planing Boats, London Research and Development Corporation."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4451","DOI":"10.1080\/01431161.2018.1425568","article-title":"Comparison of ship wake detectability on C-band and X-band SAR","volume":"39","author":"Tings","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"12293","DOI":"10.1029\/JC093iC10p12293","article-title":"Synthetic aperture radar imaging of surface ship wakes","volume":"93","author":"Lyden","year":"1988","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1080\/014311699211912","article-title":"Radar imaging of Kelvin arms of ship wakes","volume":"20","author":"Hennings","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Graziano, M.D., Grasso, M., and D\u2019 Errico, M. (2017). Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images. Remote Sens., 9.","DOI":"10.3390\/rs9111107"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dutta, A., and Zisserman, A. (2019, January 21\u201325). The VIA annotation software for images, audio and video. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3350535"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1109\/TAP.1985.1143684","article-title":"On the multilook images of moving targets by synthetic aperture radars","volume":"33","author":"Ouchi","year":"1985","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_51","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., and Girshick, R. (2019, May 19). Detectron2. 2019. Available online: https:\/\/github.com\/facebookresearch\/detectron2."},{"key":"ref_52","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_53","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_54","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). Microsoft coco: Common objects in context. European Conference on Computer Vision, Springer.","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\/13\/22\/4573\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:29:58Z","timestamp":1760167798000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4573"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,14]]},"references-count":54,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224573"],"URL":"https:\/\/doi.org\/10.3390\/rs13224573","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,14]]}}}