{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:21:31Z","timestamp":1782922891955,"version":"3.54.5"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T00:00:00Z","timestamp":1705795200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T00:00:00Z","timestamp":1705795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia Autonomous Region","doi-asserted-by":"publisher","award":["2022LHMS06005"],"award-info":[{"award-number":["2022LHMS06005"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066036"],"award-info":[{"award-number":["62066036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42106177"],"award-info":[{"award-number":["42106177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11760-023-02962-9","type":"journal-article","created":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T09:02:01Z","timestamp":1705827721000},"page":"2951-2964","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["SAR ship detection network based on global context and multi-scale feature enhancement"],"prefix":"10.1007","volume":"18","author":[{"given":"Shichuang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dahua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,21]]},"reference":[{"key":"2962_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3128060","volume":"60","author":"Xi Yang","year":"2022","unstructured":"Yang, Xi., et al.: A robust one-stage detector for multiscale ship detection with complex background in massive SAR images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201312 (2022). https:\/\/doi.org\/10.1109\/TGRS.2021.3128060","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2962_CR2","doi-asserted-by":"publisher","first-page":"8048","DOI":"10.1109\/JSTARS.2021.3102989","volume":"14","author":"X Zhang","year":"2021","unstructured":"Zhang, X., et al.: Multitask learning for ship detection from synthetic aperture radar images. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 8048\u20138062 (2021). https:\/\/doi.org\/10.1109\/JSTARS.2021.3102989","journal-title":"IEEE J. Select. Top. Appl. Earth Observ. Remote Sens."},{"issue":"12","key":"2962_CR3","doi-asserted-by":"publisher","first-page":"16921","DOI":"10.1007\/s11042-022-12243-1","volume":"81","author":"SM Idicula","year":"2022","unstructured":"Idicula, S.M., Paul, B.: A novel sarnede method for real-time ship detection from synthetic aperture radar image. Multimed. Tools Appl. 81(12), 16921\u201316944 (2022)","journal-title":"Multimed. Tools Appl."},{"key":"2962_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3145790","volume":"19","author":"Y Niu","year":"2022","unstructured":"Niu, Y., et al.: Efficient encoder\u2013decoder network with estimated direction for SAR ship detection. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2022.3145790","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"1","key":"2962_CR5","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TGRS.2002.808063","volume":"41","author":"J Schou","year":"2003","unstructured":"Schou, J., et al.: CFAR edge detector for polarimetric SAR images. IEEE Trans. Geosci. Remote Sens. 41(1), 20\u201332 (2003). https:\/\/doi.org\/10.1109\/TGRS.2002.808063","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2962_CR6","doi-asserted-by":"publisher","unstructured":"Kang, M., et al.: A modified faster R-CNN based on CFAR algorithm for SAR ship detection. In: 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), pp. 1\u20134 (2017). https:\/\/doi.org\/10.1109\/RSIP.2017.7958815","DOI":"10.1109\/RSIP.2017.7958815"},{"issue":"4","key":"2962_CR7","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/s11760-018-1408-4","volume":"13","author":"S Chen","year":"2019","unstructured":"Chen, S., Li, X.: A new CFAR algorithm based on variable window for ship target detection in SAR images. Signal Image Video Process. 13(4), 779\u2013786 (2019)","journal-title":"Signal Image Video Process."},{"issue":"1","key":"2962_CR8","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/JSEE.2012.00006","volume":"23","author":"Y Cui","year":"2012","unstructured":"Cui, Y., Yang, J., Zhang, X.: New CFAR target detector for SAR images based on kernel density estimation and mean square error distance. J. Syst. Eng. Electron. 23(1), 40\u201346 (2012). https:\/\/doi.org\/10.1109\/JSEE.2012.00006","journal-title":"J. Syst. Eng. Electron."},{"issue":"10","key":"2962_CR9","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.3390\/rs13101995","volume":"13","author":"P Xu","year":"2021","unstructured":"Xu, P., et al.: On-board real-time ship detection in HISEA-1 SAR images based on CFAR and lightweight deep learning. Remote Sens. 13(10), 1995 (2021)","journal-title":"Remote Sens."},{"key":"2962_CR10","first-page":"45","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., et al.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 45 (2015)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2962_CR11","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2962_CR12","first-page":"32","volume":"29","author":"J Dai","year":"2016","unstructured":"Dai, J., et al.: R-fcn: object detection via region-based fully convolutional networks. Adv. Neural. Inf. Process. Syst. 29, 32 (2016)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2962_CR13","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: Ssd: single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14. Springer, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2962_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"2962_CR15","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: Efficient: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"issue":"3","key":"2962_CR16","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s11063-022-10753-5","volume":"54","author":"M Zhu","year":"2022","unstructured":"Zhu, M., et al.: FSFADet: arbitrary-oriented ship detection for SAR images based on feature separation and feature alignment. Neural. Process. Lett. 54(3), 1995\u20132005 (2022)","journal-title":"Neural. Process. Lett."},{"issue":"1","key":"2962_CR17","doi-asserted-by":"publisher","first-page":"016511","DOI":"10.1117\/1.JRS.17.016511","volume":"17","author":"M Zhang","year":"2023","unstructured":"Zhang, M., et al.: Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network. J. Appl. Remote. Sens. 17(1), 016511\u2013016511 (2023)","journal-title":"J. Appl. Remote. Sens."},{"key":"2962_CR18","doi-asserted-by":"publisher","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","volume":"13","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., et al.: Attention receptive pyramid network for ship detection in SAR images. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 13, 2738\u20132756 (2020). https:\/\/doi.org\/10.1109\/JSTARS.2020.2997081","journal-title":"IEEE J. Select. Top. Appl. Earth Observ. Remote Sens."},{"key":"2962_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3062038","volume":"60","author":"D Li","year":"2022","unstructured":"Li, D., et al.: A novel multidimensional domain deep learning network for SAR ship detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201313 (2022). https:\/\/doi.org\/10.1109\/TGRS.2021.3062038","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2962_CR20","unstructured":"Ge, Z., et al.: Yolox: exceeding yolo series in 2021 (2021). arXiv preprint arXiv:2107.08430"},{"key":"2962_CR21","doi-asserted-by":"crossref","unstructured":"Cao, Y., et al.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 0\u20130 (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"2962_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2962_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2962_CR24","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"key":"2962_CR25","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., et al.: Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 385\u2013400 (2018)","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"2962_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, Z., et al.: Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(07), pp. 12993\u201313000 (2020)","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"2962_CR27","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., Shao, J.: Ship detection in SAR images based on an improved faster R-CNN. In: 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"issue":"7","key":"2962_CR28","doi-asserted-by":"publisher","first-page":"765","DOI":"10.3390\/rs11070765","volume":"11","author":"Y Wang","year":"2019","unstructured":"Wang, Y., et al.: A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sens. 11(7), 765 (2019)","journal-title":"Remote Sens."},{"issue":"14","key":"2962_CR29","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.3390\/rs13142771","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang, T., Zhang, X., Ke, X.: Quad-FPN: a novel quad feature pyramid network for SAR ship detection. Remote Sens. 13(14), 2771 (2021)","journal-title":"Remote Sens."},{"key":"2962_CR30","doi-asserted-by":"crossref","unstructured":"Carion, N., et al.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"2962_CR31","unstructured":"Bochkovskiy, A., Wang, C.Y., Mark Liao, H.Y.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"2962_CR32","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)","DOI":"10.1109\/CVPR52729.2023.00721"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02962-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02962-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02962-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T20:05:46Z","timestamp":1710878746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02962-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,21]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2962"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02962-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,21]]},"assertion":[{"value":"26 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve human or animal subjects, and thus, no ethical approval was required. The study protocol adhered to the guidelines established by the journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}