{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:08:16Z","timestamp":1772554096665,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection in aerial images has received extensive attention in recent years. The current mainstream anchor-based methods directly divide the training samples into positives and negatives according to the intersection-over-unit (IoU) of the preset anchors. This label assignment strategy assigns densely arranged samples for training, which leads to a suboptimal learning process and cause the model to suffer serious duplicate detections and missed detections. In this paper, we propose a sparse label assignment strategy (SLA) to select high-quality sparse anchors based on the posterior IoU of detections. In this way, the inconsistency between classification and regression is alleviated, and better performance can be achieved through balanced training. Next, to accurately detect small and densely arranged objects, we use a position-sensitive feature pyramid network (PS-FPN) with a coordinate attention module to extract position-sensitive features for accurate localization. Finally, the distance rotated IoU loss is proposed to eliminate the inconsistency between the training loss and the evaluation metric for better bounding box regression. Extensive experiments on the DOTA, HRSC2016, and UCAS-AOD datasets demonstrate the superiority of the proposed approach.<\/jats:p>","DOI":"10.3390\/rs13142664","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T12:31:25Z","timestamp":1625661085000},"page":"2664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Sparse Label Assignment for Oriented Object Detection in Aerial Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7596-171X","authenticated-orcid":false,"given":"Qi","family":"Ming","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1782-4535","authenticated-orcid":false,"given":"Lingjuan","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6871-8236","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Junjie","family":"Song","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7084-9101","authenticated-orcid":false,"given":"Xue","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"ref_1","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. arXiv."},{"key":"ref_3","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_4","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_5","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_6","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhong, J., and Tan, Y. (2019). Multiple-oriented and small object detection with convolutional neural networks for aerial image. Remote Sens., 11.","DOI":"10.3390\/rs11182176"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qian, X., Lin, S., Cheng, G., Yao, X., Ren, H., and Wang, W. (2020). Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12010143"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhong, B., and Ao, K. (2020). Single-Stage Rotation-Decoupled Detector for Oriented Object. Remote Sens., 12.","DOI":"10.3390\/rs12193262"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1109\/TGRS.2019.2912301","article-title":"Road detection and centerline extraction via deep recurrent convolutional neural network U-Net","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3518","DOI":"10.1109\/TGRS.2020.3018106","article-title":"GRS-Det: An Anchor-Free Rotation Ship Detector Based on Gaussian-Mask in Remote Sensing Images","volume":"59","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remot. Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2018.2856921","article-title":"Toward arbitrary-oriented ship detection with rotated region proposal and discrimination networks","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/TGRS.2020.2995477","article-title":"A Novel CNN-Based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018, January 8\u201314). Acquisition of localization confidence for accurate object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, Y., Zhu, C., Wang, J., Savvides, M., and Zhang, X. (2019, January 15\u201319). Bounding box regression with uncertainty for accurate object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00300"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Choi, J., Chun, D., Kim, H., and Lee, H.J. (November, January 27). Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00059"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ming, Q., Zhou, Z., Miao, L., Zhang, H., and Li, L. (2021, January 2\u20139). Dynamic Anchor Learning for Arbitrary-Oriented Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Palo Alto, CA, USA.","DOI":"10.1609\/aaai.v35i3.16336"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yuan, L., Weng, L., and Yang, Y. (2017, January 24\u201326). A high resolution optical satellite image dataset for ship recognition and some new baselines. Proceedings of the International Conference on Pattern Recognition Applications and Methods, Porto, Portugal.","DOI":"10.5220\/0006120603240331"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_23","unstructured":"Li, B., Liu, Y., and Wang, X. (February, January 27). Gradient harmonized single-stage detector. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201320). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feng, Y., Diao, W., Sun, X., Yan, M., and Gao, X. (2019). Towards automated ship detection and category recognition from high-resolution aerial images. Remote Sens., 11.","DOI":"10.3390\/rs11161901"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","article-title":"CAD-Net: A context-aware detection network for objects in remote sensing imagery","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TGRS.2019.2954328","article-title":"FMSSD: Feature-merged single-shot detection for multiscale objects in large-scale remote sensing imagery","volume":"58","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ming, Q., Miao, L., Zhou, Z., and Dong, Y. (2021). CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images. arXiv.","DOI":"10.1109\/TGRS.2021.3095186"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.isprsjprs.2020.01.025","article-title":"Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images","volume":"161","author":"Fu","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, X., and Yan, J. (2020). Arbitrary-Oriented Object Detection with Circular Smooth Label. arXiv.","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., and Yan, J. (2020). Dense Label Encoding for Boundary Discontinuity Free Rotation Detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"ref_33","unstructured":"Qian, W., Yang, X., Peng, S., Guo, Y., and Yan, J. (2019). Learning modulated loss for rotated object detection. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ming, Q., Zhou, Z., Miao, L., Yang, X., and Dong, Y. (2021). Optimization for Oriented Object Detection via Representation Invariance Loss. arXiv.","DOI":"10.1109\/LGRS.2021.3115110"},{"key":"ref_35","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (2021). Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7247","DOI":"10.1109\/TGRS.2020.2981203","article-title":"Adaptive period embedding for representing oriented objects in aerial images","volume":"58","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","unstructured":"Zhang, X., Wan, F., Liu, C., Ji, R., and Ye, Q. (2019). Freeanchor: Learning to match anchors for visual object detection. arXiv, pp. 147\u2013155."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cao, Y., Chen, K., Loy, C.C., and Lin, D. (2020, January 13\u201319). Prime sample attention in object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01160"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Wang, K., Wan, Q., Tan, X., Xu, C., and Xia, F. (2021). A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection. Remote Sens., 13.","DOI":"10.3390\/rs13010073"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ke, W., Zhang, T., Huang, Z., Ye, Q., Liu, J., and Huang, D. (2020, January 13\u201319). Multiple anchor learning for visual object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01022"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wan, F., Liu, C., Ji, X., and Ye, Q. (2021). Learning to match anchors for visual object detection. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3050494"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021). Coordinate attention for efficient mobile network design. arXiv.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, 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.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., and Jiao, J. (2015, January 27\u201330). Orientation robust object detection in aerial images using deep convolutional neural network. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-oriented scene text detection via rotation proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_50","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, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding vertex on the horizontal bounding box for multi-oriented object detection","volume":"43","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/JSTARS.2020.3036685","article-title":"Learning Point-guided Localization for Detection in Remote Sensing Images","volume":"14","author":"Song","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liao, M., Zhu, Z., Shi, B., Xia, G.s., and Bai, X. (2018, January 18\u201323). Rotation-sensitive regression for oriented scene text detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00619"},{"key":"ref_54","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_55","unstructured":"Yang, X., Liu, Q., Yan, J., Li, A., Zhang, Z., and Yu, G. (2019). R3det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. arXiv."},{"key":"ref_56","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","article-title":"Oriented objects as pairs of middle lines","volume":"169","author":"Wei","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","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\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01122"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:03Z","timestamp":1760164023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":58,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142664"],"URL":"https:\/\/doi.org\/10.3390\/rs13142664","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]}}}