{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:32:56Z","timestamp":1773909176129,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"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":["41976169"],"award-info":[{"award-number":["41976169"]}],"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>Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map\u2019s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship\u2019s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN\u2019s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.<\/jats:p>","DOI":"10.3390\/rs15030626","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T04:19:22Z","timestamp":1674447562000},"page":"626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["D-MFPN: A Doppler Feature Matrix Fused with a Multilayer Feature Pyramid Network for SAR Ship Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Yucheng","family":"Zhou","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Fu","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Han","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2445-0359","authenticated-orcid":false,"given":"Junxin","family":"Yang","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3300","authenticated-orcid":false,"given":"Zongxu","family":"Pan","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Hu","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Yin","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 System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3277","DOI":"10.1109\/TGRS.2016.2514494","article-title":"Ground-moving target imaging and velocity estimation based on mismatched compression for bistatic forward-looking SAR","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/LGRS.2019.2920668","article-title":"Ship detection with superpixel-level Fisher vector in high-resolution SAR images","volume":"17","author":"Lin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/LGRS.2016.2631638","article-title":"Synthetic aperture radar ship detection using Haar-like features","volume":"14","author":"Schwegmann","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/LGRS.2017.2789204","article-title":"A novel automatic PolSAR ship detection method based on superpixel-level local information measurement","volume":"15","author":"He","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TGRS.2016.2606481","article-title":"New hierarchical saliency filtering for fast ship detection in high-resolution SAR images","volume":"55","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Song, S., Xu, B., and Yang, J. (2016). SAR target recognition via supervised discriminative dictionary learning and sparse representation of the SAR-HOG feature. Remote Sens., 8.","DOI":"10.3390\/rs8080683"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TGRS.2019.2931353","article-title":"CFAR ship detection in polarimetric synthetic aperture radar images based on whitening filter","volume":"58","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/LGRS.2015.2412174","article-title":"A bilateral CFAR algorithm for ship detection in SAR images","volume":"12","author":"Leng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/LGRS.2014.2332076","article-title":"Target recognition in SAR images via classification on Riemannian manifolds","volume":"12","author":"Dong","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/7.937460","article-title":"Improved SAR target detection via extended fractal features","volume":"37","author":"Kaplan","year":"2001","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images","volume":"47","author":"Gao","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Ao, W., and Xu, F. (2018, January 26\u201328). In Robust Ship Detection in SAR Images from Complex Background. Proceedings of the 2018 IEEE International Conference on Computational Electromagnetics (ICCEM), Chengdu, China.","DOI":"10.1109\/COMPEM.2018.8496647"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103514","DOI":"10.1016\/j.dsp.2022.103514","article-title":"A survey of modern deep learning based object detection models","volume":"126","author":"Zaidi","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","article-title":"Attention receptive pyramid network for ship detection in SAR images","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6083","DOI":"10.1109\/JSTARS.2021.3087555","article-title":"Multi-scale ship detection from SAR and optical imagery via a more accurate YOLOv3","volume":"14","author":"Hong","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","first-page":"1","article-title":"Semantic context-aware network for multiscale object detection in remote sensing images","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., and Shao, J. (2017, January 13\u201314). In 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_20","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"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":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, J., Fu, X., Qin, R., Wang, X., and Ma, Z. (2021). High-speed lightweight ship detection algorithm based on YOLO-v4 for three-channels RGB SAR image. Remote Sens., 13.","DOI":"10.3390\/rs13101909"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). In Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, K., Zhang, M., Xu, M., Tang, R., Wang, L., and Wang, H. (2022). Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14133186"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). In 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_27","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/JMASS.2022.3203214","article-title":"RetinaNet-Based Compact Polarization SAR Ship Detection","volume":"3","author":"Gao","year":"2022","journal-title":"IEEE J. Miniaturization Air Space Syst."},{"key":"ref_28","first-page":"1922","article-title":"Fcos: A simple and strong anchor-free object detector","volume":"44","author":"Tian","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, M., Hu, G., Zhou, H., Wang, S., Feng, Z., and Yue, S. (2022). A Ship Detection Method via Redesigned FCOS in Large-Scale SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14051153"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). In 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_31","doi-asserted-by":"crossref","unstructured":"Zhu, H., Xie, Y., Huang, H., Jing, C., Rong, Y., Zhu, H., Xie, Y., Huang, H., Jing, C., and Rong, Y. (2021). DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images. Sensors, 21.","DOI":"10.3390\/s21238146"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Leng, X., Wang, J., Ji, K., and Kuang, G. (2022, January 17\u201322). In Ship Detection in Range-Compressed SAR Data. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884909"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108365","DOI":"10.1016\/j.patcog.2021.108365","article-title":"A polarization fusion network with geometric feature embedding for SAR ship classification","volume":"123","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","article-title":"Complex-valued convolutional neural network and its application in polarimetric SAR image classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.isprsjprs.2020.01.016","article-title":"Deep SAR-Net: Learning objects from signals","volume":"161","author":"Huang","year":"2020","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). In Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Yang, S., Loy, C.C., and Lin, D. (2019, January 15\u201320). In Region proposal by guided anchoring. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00308"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/626\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:12:08Z","timestamp":1760119928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,20]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030626"],"URL":"https:\/\/doi.org\/10.3390\/rs15030626","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,20]]}}}