{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:31:10Z","timestamp":1775176270057,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021KJY11"],"award-info":[{"award-number":["2021KJY11"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202214050002344"],"award-info":[{"award-number":["202214050002344"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Shenzhen Science and Technology Program","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Shenzhen Science and Technology Program","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Shenzhen Science and Technology Program","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Shenzhen Science and Technology Program","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Shenzhen Science and Technology Program","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021KJY11"],"award-info":[{"award-number":["2021KJY11"]}]},{"name":"Shenzhen Science and Technology Program","award":["202214050002344"],"award-info":[{"award-number":["202214050002344"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["2021KJY11"],"award-info":[{"award-number":["2021KJY11"]}]},{"name":"Science and Technology on Near-Surface Detection Laboratory Pre-Research Foundation","award":["202214050002344"],"award-info":[{"award-number":["202214050002344"]}]},{"name":"National Natural Science Foundation of China","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"National Natural Science Foundation of China","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"National Natural Science Foundation of China","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"National Natural Science Foundation of China","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"National Natural Science Foundation of China","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"National Natural Science Foundation of China","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"National Natural Science Foundation of China","award":["2021KJY11"],"award-info":[{"award-number":["2021KJY11"]}]},{"name":"National Natural Science Foundation of China","award":["202214050002344"],"award-info":[{"award-number":["202214050002344"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["2021KJY11"],"award-info":[{"award-number":["2021KJY11"]}]},{"name":"Science and Technology Talents Foundation Project of Air Force Early Warning Academy","award":["202214050002344"],"award-info":[{"award-number":["202214050002344"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["2021A1515010768"],"award-info":[{"award-number":["2021A1515010768"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["202206193000001"],"award-info":[{"award-number":["202206193000001"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["20220815171723002"],"award-info":[{"award-number":["20220815171723002"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["6142414200607"],"award-info":[{"award-number":["6142414200607"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["62001523"],"award-info":[{"award-number":["62001523"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["62203465"],"award-info":[{"award-number":["62203465"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["2021KJY11"],"award-info":[{"award-number":["2021KJY11"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["202214050002344"],"award-info":[{"award-number":["202214050002344"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the limitations of the horizontal bounding boxes for locating the oriented ship targets in synthetic aperture radar (SAR) images, the rotated bounding box (RBB) has received wider attention in recent years. First, the existing RBB encodings suffer from boundary discontinuity problems, which interfere with the convergence of the model, and then lead to some problems, such as the inaccurate location of the ship targets in the boundary state. Thus, from the perspective that the long-edge features of the ships are more representative of their orientation, the long-edge decomposition RBB encoding has been proposed in this paper, which can avoid the boundary discontinuity problem. Second, the problem of the positive and negative samples imbalance is serious for the SAR ship images because only a few ship targets exist in the vast background of these images. Since the ship targets of different sizes are subject to varying degrees of interference caused by this problem, a multiscale elliptical Gaussian sample balancing strategy has been proposed in this paper, which can mitigate the impact of this problem by labeling the loss weights of the negative samples within the target foreground area with multiscale elliptical Gaussian kernels. Finally, experiments based on the CenterNet model were implemented on the benchmark SAR image dataset SSDD (SAR ship detection dataset). The experimental results demonstrate that our proposed long-edge decomposition RBB encoding outperforms other conventional RBB encodings in the task of oriented ship detection in SAR images. In addition, our proposed multiscale elliptical Gaussian sample balancing strategy is effective and can improve the model performance.<\/jats:p>","DOI":"10.3390\/rs15030673","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T04:50:41Z","timestamp":1674449441000},"page":"673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Xinqiao","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}]},{"given":"Hongtu","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}]},{"given":"Jiaxing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Guoqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, China"}]},{"given":"Kai","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1109\/TGRS.2016.2516046","article-title":"Efficient raw signal generation based on equivalent scatterer and subaperture processing for one-stationary bistatic SAR including motion errors","volume":"54","author":"Xie","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/JSTARS.2016.2639580","article-title":"Fast factorized backprojection algorithm for one-stationary bistatic spotlight circular SAR image formation","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"31143","DOI":"10.1109\/ACCESS.2020.2971660","article-title":"High-efficiency and high-precision reconstruction strategy for P-band ultra-wideband bistatic synthetic aperture radar raw data including motion errors","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8668","DOI":"10.1109\/JSEN.2019.2922649","article-title":"Refocusing of ground moving target in circular synthetic aperture radar","volume":"19","author":"An","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8642","DOI":"10.1109\/JSEN.2019.2912863","article-title":"A novel method for single-channel CSAR ground moving target imaging","volume":"19","author":"Li","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1109\/TGRS.2019.2899337","article-title":"Ship detection in high-resolution SAR images by clustering spatially enhanced pixel descriptor","volume":"57","author":"Lang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","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_8","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_9","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_10","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 (ICCV), Seoul, Republic of Korea."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","article-title":"CornerNet: Detecting objects as paired keypoints","volume":"128","author":"Law","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_13","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_14","first-page":"841","article-title":"High-speed and high-accurate SAR ship detection based on a depthwise separable convolution neural network","volume":"8","author":"Xiaoling","year":"2019","journal-title":"J. Radars"},{"key":"ref_15","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_16","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 multiscale ship detection in SAR images","volume":"59","author":"Fu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107787","DOI":"10.1016\/j.patcog.2020.107787","article-title":"A CenterNet++ model for ship detection in SAR images","volume":"112","author":"Guo","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8333","DOI":"10.1109\/TGRS.2019.2920534","article-title":"DRBox-v2: An improved detector with rotatable boxes for target detection in SAR images","volume":"57","author":"An","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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_20","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 (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/JSTARS.2021.3049851","article-title":"A novel CNN-based detector for ship detection based on rotatable bounding box in SAR images","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., and Yan, J. (2021, January 19\u201325). Dense label encoding for boundary discontinuity free rotation detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, X., and Yan, J. (2020, January 23\u201328). Arbitrary-oriented object detection with circular smooth label. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 3\u20138). Oriented object detection in aerial images with box boundary-aware vectors. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1109\/JSTARS.2021.3068530","article-title":"Learning polar encodings for arbitrary-oriented ship detection in SAR images","volume":"14","author":"He","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (2022). Rethinking rotated object detection with gaussian wasserstein distance loss. arXiv."},{"key":"ref_27","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via kullback-leibler divergence","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (2016, January 27\u201330). Training region-based object detectors with online hard example mining. Proceedings of the IEEE Conference on Computer Vision and Pattern recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref_29","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, C., Chen, F., Shen, Z., and Savvides, M. (2020, January 23\u201328). Soft anchor-point object detection. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58545-7_6"},{"key":"ref_31","first-page":"1","article-title":"A lightweight, arbitrary-oriented SAR ship detector via feature map-based knowledge distillation","volume":"11","author":"Shiqi","year":"2022","journal-title":"J. Radars"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning RoI transformer for oriented object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2018, January 18\u201323). Deep layer aggregation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00255"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan, H., Guo, X., Ma, C., and Xu, C. (2020, January 13\u201319). Dynamic refinement network for oriented and densely packed object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01122"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. (2021, January 20\u201325). Redet: A rotation-equivariant detector for aerial object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 11\u201317). Oriented R-CNN for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/673\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:14:04Z","timestamp":1760120044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,23]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030673"],"URL":"https:\/\/doi.org\/10.3390\/rs15030673","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,23]]}}}