{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T22:05:38Z","timestamp":1766181938579,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,6]],"date-time":"2022-11-06T00:00:00Z","timestamp":1667692800000},"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":["61971101"],"award-info":[{"award-number":["61971101"]}],"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>Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy.<\/jats:p>","DOI":"10.3390\/rs14215596","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T03:02:22Z","timestamp":1667790142000},"page":"5596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["UltraHi-PrNet: An Ultra-High Precision Deep Learning Network for Dense Multi-Scale Target Detection in SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5559-158X","authenticated-orcid":false,"given":"Zheng","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1155-786X","authenticated-orcid":false,"given":"Zongyong","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhipeng","family":"Zang","sequence":"additional","affiliation":[{"name":"Beijing Huahang Radio Measurement Research Institute, Beijing 102445, China"}]},{"given":"Xiangjie","family":"Meng","sequence":"additional","affiliation":[{"name":"Beijing Huahang Radio Measurement Research Institute, Beijing 102445, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0117-9087","authenticated-orcid":false,"given":"Zongjie","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6565","DOI":"10.1109\/TGRS.2020.2977982","article-title":"Bistatic Forward-Looking SAR MP-DPCA Method for Space\u2013Time Extension Clutter Suppression","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"1","article-title":"Hybrid SAR-ISAR Image Formation via Joint FrFT-WVD Processing for BFSAR Ship Target High-Resolution Imaging","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"11","article-title":"Performance of a high-resolution polarimetric SAR automatic target recognition system","volume":"6","author":"Novak","year":"1993","journal-title":"Linc. Lab. J."},{"key":"ref_4","first-page":"116","article-title":"Deep convolutional neural networks for ATR from SAR imagery","volume":"Volume 9475","author":"Morgan","year":"2015","journal-title":"Proceedings of the Algorithms for Synthetic Aperture Radar Imagery XXII"},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LGRS.2012.2210189","article-title":"Texture-based airport runway detection","volume":"10","author":"Aytekin","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J. Jpn. Soc. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TAES.2007.357120","article-title":"Adaptive boosting for SAR automatic target recognition","volume":"43","author":"Sun","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2408","DOI":"10.1109\/LGRS.2015.2479681","article-title":"A novel airport detection method via line segment classification and texture classification","volume":"12","author":"Tang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","unstructured":"Oliver, C., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, SciTech Publishing."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1109\/JSTARS.2017.2669335","article-title":"Multiresolution airport detection via hierarchical reinforcement learning saliency model","volume":"10","author":"Zhao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","first-page":"811","article-title":"Multilayer CFAR detection of ship targets in very high resolution SAR images","volume":"12","author":"Hou","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/LGRS.2015.2425873","article-title":"PolSAR ship detection based on superpixel-level scattering mechanism distribution features","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_18","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Brunot, A., Cortes, C., Drucker, H., Boser, B., Henderson, D., Guyon, I., and Sackinger, E. (2022, September 14). LeNet-5, Convolutional Neural Networks. Available online: http:\/\/yann.lecun.com\/exdb\/lenet."},{"key":"ref_19","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE international Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, N., Cao, Z., Cui, Z., Pi, Y., and Dang, S. (2019). Multi-scale proposal generation for ship detection in SAR images. Remote Sens., 11.","DOI":"10.3390\/rs11050526"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/LGRS.2016.2618604","article-title":"A modified CFAR algorithm based on object proposals for ship target detection in SAR images","volume":"13","author":"Dai","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","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":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"47664","DOI":"10.1109\/ACCESS.2020.2979260","article-title":"A traffic surveillance multi-scale vehicle detection object method base on encoder-decoder","volume":"8","author":"Hong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_29","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_30","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_31","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"93058","DOI":"10.1109\/ACCESS.2020.2993998","article-title":"An efficient feature pyramid network for object detection in remote sensing imagery","volume":"8","author":"Qingyun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9325","DOI":"10.1109\/ACCESS.2020.2964540","article-title":"Attention mask R-CNN for ship detection and segmentation from remote sensing images","volume":"8","author":"Nie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","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 International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","first-page":"1585","article-title":"SAR Unlabeled Target Recognition Based on Updating CNN With Assistant Decision","volume":"15","author":"Cui","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_40","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_41","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). {TensorFlow}: A System for {Large-Scale} Machine Learning. Proceedings of the 12th USENIX symposium on operating systems design and implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_42","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., and Shao, J. (2017, January 13\u201314). 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_45","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_46","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TGRS.2020.2997200","article-title":"Ship detection in large-scale SAR images via spatial shuffle-group enhance attention","volume":"59","author":"Cui","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_49","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5596\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:31Z","timestamp":1760145091000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,6]]},"references-count":49,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215596"],"URL":"https:\/\/doi.org\/10.3390\/rs14215596","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,6]]}}}