{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T02:53:37Z","timestamp":1774580017778,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"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":["No.51276151"],"award-info":[{"award-number":["No.51276151"]}],"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>As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challenging to acquire and more expensive to label. Obtaining a large amount of high-quality aerial image data for object recognition and image understanding is an urgent problem. Existing studies show that synthetic data can effectively reduce the amount of training data required. Therefore, in this paper, we propose the first synthetic aerial image dataset for ship recognition, called UnityShip. This dataset contains over 100,000 synthetic images and 194,054 ship instances, including 79 different ship models in ten categories and six different large virtual scenes with different time periods, weather environments, and altitudes. The annotations include environmental information, instance-level horizontal bounding boxes, oriented bounding boxes, and the type and ID of each ship. This provides the basis for object detection, oriented object detection, fine-grained recognition, and scene recognition. To investigate the applications of UnityShip, the synthetic data were validated for model pre-training and data augmentation using three different object detection algorithms and six existing real-world ship detection datasets. Our experimental results show that for small-sized and medium-sized real-world datasets, the synthetic data achieve an improvement in model pre-training and data augmentation, showing the value and potential of synthetic data in aerial image recognition and understanding tasks.<\/jats:p>","DOI":"10.3390\/rs13244999","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:46:58Z","timestamp":1639086418000},"page":"4999","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Boyong","family":"He","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}]},{"given":"Xianjiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}]},{"given":"Enhui","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Weijie","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4558-0462","authenticated-orcid":false,"given":"Liaoni","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1007\/s11263-020-01316-z","article-title":"The open images dataset v4","volume":"128","author":"Kuznetsova","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., and Sun, J. (2019, January 27\u201328). Objects365: A large-scale, high-quality dataset for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00852"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","article-title":"Vehicle detection in aerial imagery: A small target detection benchmark","volume":"34","author":"Razakarivony","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mundhenk, T.N., Konjevod, G., Sakla, W.A., and Boakye, K. (2016, January 11\u201314). A large contextual dataset for classification, detection and counting of cars with deep learning. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_48"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/LGRS.2016.2565705","article-title":"Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","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\u201322). 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_9","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/TIP.2017.2773199","article-title":"Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images","volume":"27","author":"Zou","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","unstructured":"Zhu, M., Hu, J., Pu, Z., Cui, Z., Yan, L., and Wang, Y. (2019). Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shah, S., Dey, D., Lovett, C., and Kapoor, A. (2017, January 12\u201315). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Proceedings of the Field and Service Robotics, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, W., Pan, C., Zhang, R., Ren, J., Ma, Y., Fang, J., Yan, F., Geng, Q., Huang, X., and Gong, H. (2019). AADS: Augmented autonomous driving simulation using data-driven algorithms. Sci. Robot., 4.","DOI":"10.1126\/scirobotics.aaw0863"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Best, A., Narang, S., Pasqualin, L., Barber, D., and Manocha, D. (2018, January 18\u201322). Autonovi-sim: Autonomous vehicle simulation platform with weather, sensing, and traffic control. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00152"},{"key":"ref_15","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., and Vasudevan, R. (2016). Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?. arXiv.","DOI":"10.1109\/ICRA.2017.7989092"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A.M. (2016, January 27\u201330). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Angus, M., ElBalkini, M., Khan, S., Harakeh, A., Andrienko, O., Reading, C., Waslander, S., and Czarnecki, K. (2018, January 18\u201322). Unlimited road-scene synthetic annotation (URSA) dataset. Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Salt Lake City, UT, USA.","DOI":"10.1109\/ITSC.2018.8569519"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, X., and Zheng, L. (2019, January 15\u201320). Dissecting person re-identification from the viewpoint of viewpoint. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00070"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liao, S., and Shao, L. (2020, January 23\u201328). Surpassing real-world source training data: Random 3d characters for generalizable person re-identification. Proceedings of the 28th ACM International Conference on Multimedia, Augsburg, Germany.","DOI":"10.1145\/3394171.3413815"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yao, Y., Zheng, L., Yang, X., Naphade, M., and Gedeon, T. (2019). Simulating content consistent vehicle datasets with attribute descent. arXiv.","DOI":"10.1007\/978-3-030-58539-6_46"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s11263-020-01398-9","article-title":"AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild","volume":"129","author":"Zhang","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","unstructured":"Airbus (2021, December 06). Airbus Ship Detection Challenge. Available online: https:\/\/www.kaggle.com\/c\/airbus-ship-detection."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1109\/TGRS.2019.2900302","article-title":"Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 27\u201328). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Heitz, G., and Koller, D. (2008, January 12\u201318). Learning spatial context: Using stuff to find things. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88682-2_4"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TPAMI.2011.94","article-title":"Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics","volume":"34","author":"Benedek","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/LGRS.2015.2439517","article-title":"Fast multiclass vehicle detection on aerial images","volume":"12","author":"Liu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate object localization in remote sensing images based on convolutional neural networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"297","DOI":"10.14358\/PERS.85.4.297","article-title":"Vehicle detection in aerial images","volume":"85","author":"Yang","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_35","unstructured":"Lam, D., Kuzma, R., McGee, K., Dooley, S., Laielli, M., Klaric, M., Bulatov, Y., and McCord, B. (2018). xView: Objects in context in overhead imagery. arXiv."},{"key":"ref_36","unstructured":"Zhu, P., Wen, L., Bian, X., Ling, H., and Hu, Q. (2018). Vision meets drones: A challenge. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., and Tian, Q. (2018, January 8\u201314). The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"ref_38","unstructured":"Martinez, M., Sitawarin, C., Finch, K., Meincke, L., Yablonski, A., and Kornhauser, A. (2017). Beyond grand theft auto V for training, testing and enhancing deep learning in self driving cars. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Richter, S.R., Hayder, Z., and Koltun, V. (2017, January 22\u201329). Playing for benchmarks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.243"},{"key":"ref_40","unstructured":"Wrenninge, M., and Unger, J. (2018). Synscapes: A photorealistic synthetic dataset for street scene parsing. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hurl, B., Czarnecki, K., and Waslander, S. (2019, January 9\u201312). Precise synthetic image and lidar (presil) dataset for autonomous vehicle perception. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813809"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M.J., Laptev, I., and Schmid, C. (2017, January 21\u201326). Learning from synthetic humans. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.492"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Roberts, M., and Paczan, N. (2020). Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding. arXiv.","DOI":"10.1109\/ICCV48922.2021.01073"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cao, Y., Zha, Z.-J., and Tao, D. (2020, January 23\u201328). Nighttime dehazing with a synthetic benchmark. Proceedings of the 28th ACM International Conference on Multimedia, Augsburg, Germany.","DOI":"10.1145\/3394171.3413763"},{"key":"ref_45","unstructured":"Xue, Z., Mao, W., and Zheng, L. (2020). Learning to simulate complex scenes. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bondi, E., Jain, R., Aggrawal, P., Anand, S., Hannaford, R., Kapoor, A., Piavis, J., Shah, S., Joppa, L., and Dilkina, B. (2020, January 1\u20135). BIRDSAI: A dataset for detection and tracking in aerial thermal infrared videos. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093284"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, L., Liu, F., Zhao, Y., Wang, W., Yuan, X., and Zhu, J. (August, January 31). Valid: A comprehensive virtual aerial image dataset. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197186"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kong, F., Huang, B., Bradbury, K., and Malof, J. (2020, January 1\u20135). The Synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093339"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shermeyer, J., Hossler, T., Van Etten, A., Hogan, D., Lewis, R., and Kim, D. (2020). RarePlanes: Synthetic Data Takes Flight. arXiv.","DOI":"10.1109\/WACV48630.2021.00025"},{"key":"ref_50","unstructured":"Clement, N., Schoen, A., Boedihardjo, A., and Jenkins, A. (2021). Synthetic Data and Hierarchical Object Detection in Overhead Imagery. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7981","DOI":"10.1109\/LRA.2021.3101879","article-title":"ESPADA: Extended Synthetic and Photogrammetric Aerial-image Dataset","volume":"6","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Uddin, M.S., Hoque, R., Islam, K.A., Kwan, C., Gribben, D., and Li, J. (2021). Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance. Remote Sens., 13.","DOI":"10.3390\/rs13163257"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, J., Weng, L., and Yang, Y. (2017, January 13\u201317). Rotated region based CNN for ship detection. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296411"},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/LGRS.2018.2813094","article-title":"Arbitrary-oriented ship detection framework in optical remote-sensing images","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7147","DOI":"10.1109\/TGRS.2018.2848901","article-title":"HSF-Net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cheng, D., Zhang, Z., Lin, S., and Dai, J. (2019, January 27\u201328). An empirical study of spatial attention mechanisms in deep networks. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00679"},{"key":"ref_58","unstructured":"Qi, S., Wu, J., Zhou, Q., and Kang, M. (2018). Low-resolution ship detection from high-altitude aerial images. MIPPR 2017: Automatic Target Recognition and Navigation, International Society for Optics and Photonics."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, L., Wang, Z., Yu, Y., Liu, X., and Xu, F. (2020). Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12203316"},{"key":"ref_60","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."},{"key":"ref_61","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_62","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_63","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018, January 8\u201314). Group normalization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_1"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4999\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:43:54Z","timestamp":1760168634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4999"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,9]]},"references-count":63,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13244999"],"URL":"https:\/\/doi.org\/10.3390\/rs13244999","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,9]]}}}