{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:32:52Z","timestamp":1773909172405,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"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":["61971326"],"award-info":[{"award-number":["61971326"]}],"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>Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).<\/jats:p>","DOI":"10.3390\/rs13142743","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T04:26:06Z","timestamp":1626150366000},"page":"2743","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Kun","family":"Sun","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Liang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaorui","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Huai","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/LGRS.2016.2633548","article-title":"Ship detection for complex background SAR images based on a multi-scale variance weighted image entropy method","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TGRS.2010.2071879","article-title":"Ship surveillance with TerraSAR-X","volume":"49","author":"Brusch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TGRS.2013.2282820","article-title":"An improved iterative censoring scheme for CFAR ship detection with SAR imagery","volume":"52","author":"An","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/TGRS.2009.2037432","article-title":"The TerraSAR-X satellite","volume":"48","author":"Pitz","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s11760-016-0879-4","article-title":"Adaptive ship detection in SAR images using variance WIE-based method","volume":"10","author":"Wang","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A high-resolution SARimages dataset for ship detection and instance segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/JSTARS.2017.2787573","article-title":"Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images","volume":"11","author":"Ao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/76.905991","article-title":"Active contour model with gradient directional information: Directional snake","volume":"11","author":"Park","year":"2001","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_13","first-page":"713","article-title":"A fast algorithm for multilevel thresholding","volume":"17","author":"Liao","year":"2001","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/JSTARS.2017.2764506","article-title":"An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images","volume":"11","author":"Li","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/LGRS.2016.2616187","article-title":"Inshore ship detection via saliency and context information in high-resolution SAR images","volume":"13","author":"Zhai","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/TCYB.2014.2356200","article-title":"Visual-path-attention-aware saliency detection","volume":"45","author":"Jian","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_17","first-page":"7","article-title":"Target detection in synthetic aperture radar imagery: A state-of-the-art survey","volume":"7","author":"McGuire","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1017\/S0373463313000659","article-title":"Ship surveillance by integration of space-borne SAR and AIS\u2014Review of current research","volume":"67","author":"Zhao","year":"2014","journal-title":"J. Navigat."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2014.2362955","article-title":"Multilayer CFAR detection of ship targets in very high resolution SAR images","volume":"12","author":"Hou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/LGRS.2016.2540809","article-title":"Superpixel-based CFAR target detection for high-resolution SAR images","volume":"13","author":"Yu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1109\/JSTARS.2019.2925833","article-title":"A saliency detector for polarimetric SAR ship detection using similarity test","volume":"12","author":"Cui","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/LGRS.2017.2654450","article-title":"An intensity-space domain CFAR method for ship detection in HR SAR images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1109\/JSTARS.2015.2417756","article-title":"Manifold adaptation for constant false alarm rate ship detection in South African oceans","volume":"8","author":"Schwegmann","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/TAES.1973.309705","article-title":"False-alarm regulation in log-normal and Weibull clutter","volume":"9","author":"Goldstein","year":"1973","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1214\/aoms\/1177704481","article-title":"A generalization of the gamma distribution","volume":"33","author":"Stacy","year":"1962","journal-title":"Ann. Math. Stat."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-like algorithm for SAR images","volume":"53","author":"Dellinger","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4710","DOI":"10.1016\/j.eswa.2011.09.082","article-title":"Evolutionary RBF classifier for polarimetric SAR images","volume":"39","author":"Ince","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrel, 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_33","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 IEEE Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., and Ouyang, W. (2019, January 15\u201320). Hybrid task cascade for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_36","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_37","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_38","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fan, W., Zhou, F., Bai, X., Tao, M., and Tian, T. (2019). Ship detection using deep convolutional neural networks for PolSAR images. Remote Sens., 11.","DOI":"10.3390\/rs11232862"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2019). High-speed ship detection in SAR images based on a grid convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11101206"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A densely connected end-to-end neural network for multi-scale and multiscene SAR ship detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, C., and Jiang, W. (2018). Simultaneous ship detection and orientation estimation in SAR images based on attention module and angle regression. Sensors, 18.","DOI":"10.3390\/s18092851"},{"key":"ref_44","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_45","first-page":"751","article-title":"A novel false alarm suppression method for CNN-based SAR ship detector","volume":"16","author":"Yang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Queon, I. (2018, January 4\u20138). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1109\/TGRS.2020.3005151","article-title":"An anchor-free method based on feature balancing and refinement network for multi-scale ship detection in SAR images","volume":"59","author":"Fu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Li, L., Wang, C., Zhang, H., and Zhang, B. (2020). SAR image ship object generation and classification with improved residual conditional generative adversarial network. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"25459","DOI":"10.1109\/ACCESS.2019.2900522","article-title":"Data augmentation based on attributed scattering centers to train robust CNN for SAR ATR","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"42255","DOI":"10.1109\/ACCESS.2019.2907728","article-title":"Image data augmentation for SAR sensor via generative adversarial nets","volume":"7","author":"Cui","year":"2019","journal-title":"IEEE Access"},{"key":"ref_52","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computer-Assisted Intervention-MICCAI 2015, 18th International Conference, Munich, Germany, 5\u20139 October 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., and Arasanipalai, A. (2020). Rotate to attend: Convolutional triplet attention module. arXiv.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","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_57","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L., and Weinberger, Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hononlulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_58","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE Conference on Computer Vision, Seoul, Korea."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020). Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.01146"},{"key":"ref_60","unstructured":"Zheng, Z., Wang, P., Liu, J., and Ren, D. (2019, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_61","first-page":"1953","article-title":"Ship detection in SAR images based on convolutional neural network","volume":"40","author":"Li","year":"2018","journal-title":"Syst. Eng. Electron."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C. (2014). Microsoft COCO: Common objects in context. arXiv.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q. (2019). EcientDet: Scalable and efficient object detection. arXiv.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Xu, R., Change, L., and Lin, D. (2019). CARAFE: Content-aware reassembly of features. arXiv.","DOI":"10.1109\/ICCV.2019.00310"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 15\u201320). Mask scoring RCNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wei, S., Su, H., Ming, J., Wang, C., Yan, M., Kumar, D., Shi, J., and Zhang, X. (2020). Precise and robust ship detection for high-resolution SAR imagery based on HR-SDNet. Remote Sens., 12.","DOI":"10.3390\/rs12010167"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2743\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:29:35Z","timestamp":1760164175000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2743"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,13]]},"references-count":68,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142743"],"URL":"https:\/\/doi.org\/10.3390\/rs13142743","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,13]]}}}