{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T17:49:28Z","timestamp":1793814568826,"version":"4.6.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Research grant financed by the Polish Ministry of Defense","award":["423\/2022\/DA\/GB2"],"award-info":[{"award-number":["423\/2022\/DA\/GB2"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Small object detection in SAR imagery remains challenging due to limited availability of specialized datasets. The article presents a new SAR dataset designed for small-object detection. Due to the absence of publicly available datasets dedicated to vehicle detection on satellite radar imagery, a custom dataset containing 23,644 manually labelled vehicles was created using Capella and ICEYE imagery Also the results of an extensive comparative analysis of three YOLO architectures (versions 7, 8, and 12) in the task of detecting small vehicles in radar imagery were presented. The study also considers the influence of image filtering on detection effectiveness. Experimental results provided new insights into fine-tuning YOLO architectures specifically for detecting small objects in synthetic aperture radar (SAR) images. In addition, the SIVED (SAR Image dataset for VEhicle Detection) dataset (high-resolution airborne imagery) was used in the study. Model performance was tested under various configurations and with Lee, Frost, and GammaMAP filters. Furthermore, a detailed analysis of model stability was performed. The experimental results revealed notable differences in performance among the tested models. The YOLOv8 model achieved the highest detection performance on the SIVED dataset, with an F1-score of 0.958 and mAP@[0.5:0.95] of 0.838 in the unfiltered scenario, along with high stability with respect to changes in threshold parameters. The YOLOv12 model demonstrated its best performance after Lee filtering (F1 score\u2009=\u20090.951, mAP@[0.5:0.95]\u2009=\u20090.774), indicating a greater sensitivity to the quality of the input data. On the contrary, the YOLOv7 model exhibited high sensitivity to changes in confidence thresholds, necessitating precise parameter tuning. The conducted research has shown that YOLOv8 achieves superior detection performance on satellite radar imagery samples despite not incorporating advanced self-attention mechanisms. This work contributes significantly to automatic object detection in radar images, providing practical guidelines for selecting and configuring YOLO models according to the characteristics of the SAR data.<\/jats:p>","DOI":"10.1038\/s41598-025-28755-3","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T02:15:04Z","timestamp":1764123304000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Effectiveness of YOLO variants for small object detection in SAR images using a new dataset"],"prefix":"10.1038","volume":"15","author":[{"given":"Kinga","family":"Karwowska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakub","family":"Slesinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-3894","authenticated-orcid":false,"given":"Damian","family":"Wierzbicki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"28755_CR1","doi-asserted-by":"publisher","first-page":"6014","DOI":"10.1109\/ACCESS.2016.2611492","volume":"4","author":"K El-Darymli","year":"2016","unstructured":"El-Darymli, K., Gill, E. W., Mcguire, P., Power, D. & Moloney, C. Automatic target recognition in synthetic aperture radar imagery: A State-of-the-Art review. IEEE Access. 4, 6014\u20136058 (2016).","journal-title":"IEEE Access."},{"key":"28755_CR2","unstructured":"Bingen, K. A., Gauthier, D. & Chang, M. The 2024 Commercial Remote Sensing Global Rankings. (2024)."},{"key":"28755_CR3","doi-asserted-by":"publisher","first-page":"6283","DOI":"10.1007\/s00521-024-09422-6","volume":"36","author":"W Wei","year":"2024","unstructured":"Wei, W., Cheng, Y., He, J. & Zhu, X. A review of small object detection based on deep learning. Neural Comput. Appl. 36, 6283\u20136303 (2024).","journal-title":"Neural Comput. Appl."},{"key":"28755_CR4","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/S0146-664X(81)80018-4","volume":"15","author":"JS Lee","year":"1981","unstructured":"Lee, J. S. Refined filtering of image noise using local statistics. Comput. Graphics Image Process. 15, 380\u2013389 (1981).","journal-title":"Comput. Graphics Image Process."},{"key":"28755_CR5","doi-asserted-by":"publisher","unstructured":"Hasnaouy, H. E. & Kasapo\u011flu, N. G. Comparison of Feature Extraction Methods for Automated Target Recognition by Reducing Speckle Noise in SAR Data. In 10th International Conference on Recent Advances in Air and Space Technologies (RAST) 1\u20136 (2023). 1\u20136 https:\/\/doi.org\/10.1109\/RAST57548.2023.10197905 (2023).","DOI":"10.1109\/RAST57548.2023.10197905"},{"key":"28755_CR6","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1016\/j.patcog.2009.01.013","volume":"42","author":"S Huang","year":"2009","unstructured":"Huang, S., Liu, D., Gao, G. & Guo, X. A novel method for speckle noise reduction and ship target detection in SAR images. Pattern Recogn. 42, 1533\u20131542 (2009).","journal-title":"Pattern Recogn."},{"key":"28755_CR7","unstructured":"Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks. https:\/\/ieeexplore-1ieee-1org-100001bck0902.han.wat.edu.pl\/document\/8478780"},{"key":"28755_CR8","unstructured":"Speckle-Noise-Invariant Convolutional Neural Network for SAR Target Recognition. https:\/\/ieeexplore-1ieee-1org-100001bck0902.han.wat.edu.pl\/document\/8527544"},{"key":"28755_CR9","first-page":"1570","volume":"15","author":"Y Huang","year":"2018","unstructured":"Huang, Y. et al. SAR automatic target recognition using joint Low-Rank and sparse multiview denoising. IEEE Geosci. Remote Sens. Lett. 15, 1570\u20131574 (2018).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"28755_CR10","doi-asserted-by":"publisher","first-page":"7734","DOI":"10.1109\/TGRS.2020.2983420","volume":"58","author":"L Chen","year":"2020","unstructured":"Chen, L., Jiang, X., Li, Z., Liu, X. & Zhou, Z. Feature-Enhanced speckle reduction via Low-Rank and Space-Angle continuity for circular SAR target recognition. IEEE Trans. Geosci. Remote Sens. 58, 7734\u20137752 (2020).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"28755_CR11","doi-asserted-by":"publisher","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","volume":"57","author":"Z Cui","year":"2019","unstructured":"Cui, Z., Li, Q., Cao, Z. & Liu, N. Dense attention pyramid networks for Multi-Scale ship detection in SAR images. IEEE Trans. Geosci. Remote Sens. 57, 8983\u20138997 (2019).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"28755_CR12","doi-asserted-by":"publisher","unstructured":"Li, J., Qu, C. & Sun, J. Ship Detection in SAR Images Based on an Improved Faster R-CNN. 6 https:\/\/doi.org\/10.1109\/BIGSARDATA.2017.8124934 (2017).","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"28755_CR13","doi-asserted-by":"publisher","first-page":"631","DOI":"10.3390\/rs11060631","volume":"11","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Wu, R., Xu, K., Wang, J. & Sun, W. R-CNN-Based ship detection from high resolution remote sensing imagery. Remote Sens. 11, 631 (2019).","journal-title":"Remote Sens."},{"key":"28755_CR14","doi-asserted-by":"publisher","first-page":"21353","DOI":"10.1007\/s11042-022-13801-3","volume":"82","author":"E Arkin","year":"2023","unstructured":"Arkin, E., Yadikar, N., Xu, X., Aysa, A. & Ubul, K. A survey: object detection methods from CNN to transformer. Multimed Tools Appl. 82, 21353\u201321383 (2023).","journal-title":"Multimed Tools Appl."},{"key":"28755_CR15","doi-asserted-by":"publisher","first-page":"2712","DOI":"10.3390\/rs14112712","volume":"14","author":"J Li","year":"2022","unstructured":"Li, J., Xu, C., Su, H., Gao, L. & Wang, T. Deep learning for SAR ship detection: Past, present and future. Remote Sens. 14, 2712 (2022).","journal-title":"Remote Sens."},{"key":"28755_CR16","doi-asserted-by":"crossref","unstructured":"Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection. https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2483","DOI":"10.3390\/rs11212483"},{"key":"28755_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, J., Schroeder, J. & Redding, N. J. Small target detection using enhanced SAR images. In SMC\u201903 Conference Proceedings. IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483) vol. 1 1001\u20131006 vol.1 (2003).","DOI":"10.1109\/ICSMC.2003.1243945"},{"key":"28755_CR18","doi-asserted-by":"publisher","unstructured":"Xu, J., Han, W., He, X. & Chen, R. Small target detection in SAR image using the Alpha-stable distribution model. in International Conference on Image Analysis and Signal Processing 64\u201368 (2010). 64\u201368 https:\/\/doi.org\/10.1109\/IASP.2010.5476160 (2010).","DOI":"10.1109\/IASP.2010.5476160"},{"key":"28755_CR19","doi-asserted-by":"publisher","first-page":"112","DOI":"10.3390\/jmse8020112","volume":"8","author":"P Chen","year":"2020","unstructured":"Chen, P., Li, Y., Zhou, H., Liu, B. & Liu, P. Detection of small ship objects using anchor boxes cluster and feature pyramid network model for SAR imagery. J. Mar. Sci. Eng. 8, 112 (2020).","journal-title":"J. Mar. Sci. Eng."},{"key":"28755_CR20","doi-asserted-by":"publisher","unstructured":"Ge, R., Mao, Y., Li, S. & Wei, H. Research On Ship Small Target Detection In SAR Image Based On Improved YOLO-v7. in 2023 International Applied Computational Electromagnetics Society Symposium (ACES-China) 1\u20133 https:\/\/doi.org\/10.23919\/ACES-China60289.2023.10249265 (2023).","DOI":"10.23919\/ACES-China60289.2023.10249265"},{"key":"28755_CR21","doi-asserted-by":"publisher","first-page":"8969","DOI":"10.1109\/JSTARS.2023.3316309","volume":"16","author":"M Sun","year":"2023","unstructured":"Sun, M. et al. A fast and accurate small target detection algorithm based on feature fusion and Cross-Layer connection network for the SAR images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16, 8969\u20138981 (2023).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"28755_CR22","doi-asserted-by":"publisher","first-page":"286","DOI":"10.3390\/rs17020286","volume":"17","author":"K Wang","year":"2025","unstructured":"Wang, K., Pan, Z. & Wen, Z. S. V. D. D. D. SAR vehicle target detection dataset augmentation based on diffusion model. Remote Sens. 17, 286 (2025).","journal-title":"Remote Sens."},{"key":"28755_CR23","first-page":"1","volume":"21","author":"P Chen","year":"2024","unstructured":"Chen, P., Wang, Y. & Liu, H. GCN-YOLO: YOLO based on graph convolutional network for SAR vehicle target detection. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135 (2024).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"28755_CR24","doi-asserted-by":"publisher","first-page":"6746","DOI":"10.3390\/s24206746","volume":"24","author":"J Han","year":"2024","unstructured":"Han, J., Kang, L., Tian, J., Jiang, M. & Guo, N. Vehicle localization method in complex SAR images based on feature reconstruction and aggregation. Sensors 24, 6746 (2024).","journal-title":"Sensors"},{"key":"28755_CR25","doi-asserted-by":"publisher","first-page":"3242","DOI":"10.3390\/rs15133242","volume":"15","author":"Y Song","year":"2023","unstructured":"Song, Y. et al. Vehicle target detection method for Wide-Area SAR images based on Coarse-Grained judgment and Fine-Grained detection. Remote Sens. 15, 3242 (2023).","journal-title":"Remote Sens."},{"key":"28755_CR26","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.3390\/rs14133086","volume":"14","author":"L Li","year":"2022","unstructured":"Li, L., Du, Y. & Du, L. Vehicle target detection network in SAR images based on Rectangle-Invariant rotatable Convolution. Remote Sens. 14, 3086 (2022).","journal-title":"Remote Sens."},{"key":"28755_CR27","doi-asserted-by":"publisher","unstructured":"Keydel, E. R., Lee, S. W. & Moore, J. T. MSTAR extended operating conditions: a tutorial. in (eds. Zelnio, E. G. & Douglass, R. J.) 228\u2013242Orlando, FL, https:\/\/doi.org\/10.1117\/12.242059 (1996).","DOI":"10.1117\/12.242059"},{"key":"28755_CR28","unstructured":"Synthetic, S. A. R. Image Generation using Sensor, Terrain and Target Models. https:\/\/ieeexplore-1ieee-1org-100001bck0bcc.han.wat.edu.pl\/document\/7559326"},{"key":"28755_CR29","doi-asserted-by":"crossref","unstructured":"Lewis, B. et al. SPIE,. A SAR dataset for ATR development: the Synthetic and Measured Paired Labeled Experiment (SAMPLE). In Algorithms for Synthetic Aperture Radar Imagery XXVI vol. 10987 39\u201354 (2019).","DOI":"10.1117\/12.2523460"},{"key":"28755_CR30","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.3390\/rs15112825","volume":"15","author":"X Lin","year":"2023","unstructured":"Lin, X. et al. SIVED: A SAR image dataset for vehicle detection based on rotatable bounding box. Remote Sens. 15, 2825 (2023).","journal-title":"Remote Sens."},{"key":"28755_CR31","doi-asserted-by":"publisher","unstructured":"Li, Y. et al. SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection.  https:\/\/doi.org\/10.48550\/arXiv.2403.06534 (2024).","DOI":"10.48550\/arXiv.2403.06534"},{"key":"28755_CR32","doi-asserted-by":"publisher","unstructured":"Liu, Y. et al. ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild. https:\/\/doi.org\/10.48550\/arXiv.2501.13354 (2025).","DOI":"10.48550\/arXiv.2501.13354"},{"key":"28755_CR33","doi-asserted-by":"publisher","first-page":"486","DOI":"10.3390\/rs16030486","volume":"16","author":"H Tang","year":"2024","unstructured":"Tang, H. et al. A lightweight SAR image ship detection method based on improved Convolution and YOLOv7. Remote Sens. 16, 486 (2024).","journal-title":"Remote Sens."},{"key":"28755_CR34","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.icte.2023.03.009","volume":"10","author":"C Yu","year":"2024","unstructured":"Yu, C. & Shin, Y. SAR ship detection based on improved YOLOv5 and BiFPN. ICT Express. 10, 28\u201333 (2024).","journal-title":"ICT Express"},{"key":"28755_CR35","doi-asserted-by":"publisher","first-page":"5849","DOI":"10.3390\/s23135849","volume":"23","author":"AA Adegun","year":"2023","unstructured":"Adegun, A. A., Dombeu, F., Viriri, J. V., Odindi, J. & S. & State-of-the-Art deep learning methods for objects detection in remote sensing satellite images. Sensors 23, 5849 (2023).","journal-title":"Sensors"},{"key":"28755_CR36","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1109\/JSTARS.2024.3505964","volume":"18","author":"J Anandakrishnan","year":"2025","unstructured":"Anandakrishnan, J. et al. Precise Spatial prediction of rice seedlings from Large-Scale airborne remote sensing data using optimized Li-YOLOv9. IEEE J. Sel. Top. Appl. Earth Obs.  Remote Sens. 18, 2226\u20132238 (2025).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"28755_CR37","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.icte.2024.02.007","volume":"10","author":"C Yu","year":"2024","unstructured":"Yu, C. & Shin, Y. An efficient YOLO for ship detection in SAR images via channel shuffled reparameterized Convolution blocks and dynamic head. ICT Express. 10, 673\u2013679 (2024).","journal-title":"ICT Express"},{"key":"28755_CR38","unstructured":"HumanSignal\/labelImg. HumanSignal. (2025)."},{"key":"28755_CR39","doi-asserted-by":"publisher","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. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 7464\u20137475 https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721 (2023).","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"28755_CR40","doi-asserted-by":"publisher","unstructured":"Varghese, R. & M., S. YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. In International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)  https:\/\/doi.org\/10.1109\/ADICS58448.2024.10533619 (2024).","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"28755_CR41","doi-asserted-by":"publisher","unstructured":"Tian, Y., Ye, Q. & Doermann, D. YOLOv12: Attention-Centric Real-Time Object Detectors. https:\/\/doi.org\/10.48550\/arXiv.2502.12524 (2025).","DOI":"10.48550\/arXiv.2502.12524"},{"key":"28755_CR42","unstructured":"Zi\u00f3\u0142kowski, D. & Wo\u017aniak, E. Metoda Redukcji Plamkowania Na Obrazach Radarowych Za pomoc\u0105 Dwustopniowego filtru Warunkowego. Archiwum Fotogrametrii Kartografii I Teledetekcji 20, (2009)."},{"key":"28755_CR43","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","volume":"PAMI-2","author":"JS Lee","year":"1980","unstructured":"Lee, J. S. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2, 165\u2013168 (1980).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28755_CR44","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1080\/01431169308953999","volume":"14","author":"A LOPES","year":"1993","unstructured":"Lopes, A., Touzi, N. E. Z. R. Y. E., Laur, H. & R. & and Structure detection and statistical adaptive speckle filtering in SAR images. Int. J. Remote Sens. 14, 1735\u20131758 (1993).","journal-title":"Int. J. Remote Sens."},{"key":"28755_CR45","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/TPAMI.1982.4767223","volume":"PAMI-4","author":"VS Frost","year":"1982","unstructured":"Frost, V. S., Stiles, J. A., Shanmugan, K. S. & Holtzman, J. C. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 157\u2013166 (1982).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28755_CR46","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004).","journal-title":"IEEE Trans. Image Process."},{"key":"28755_CR47","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/97.995823","volume":"9","author":"Z Wang","year":"2002","unstructured":"Wang, Z. & Bovik, A. C. A universal image quality index. IEEE. Signal. Process. Lett. 9, 81\u201384 (2002).","journal-title":"IEEE. Signal. Process. Lett."},{"key":"28755_CR48","doi-asserted-by":"publisher","unstructured":"Keelan, B. Handbook of Image Quality: Characterization and Prediction (CRC),  https:\/\/doi.org\/10.1201\/9780203910825 (2002).","DOI":"10.1201\/9780203910825"},{"key":"28755_CR49","unstructured":"Goetz, A., Boardman, W. & Yunas, R. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Proceedings of the 3rd Annu (1992)."},{"key":"28755_CR50","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TIP.2005.859378","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh, H. R. & Bovik, A. C. Image information and visual quality. IEEE Trans. Image Process. 15, 430\u2013444 (2006).","journal-title":"IEEE Trans. Image Process."},{"key":"28755_CR51","unstructured":"Van Rijsbergen, C. J. & Information Retrieval Inform.  Retrieval https:\/\/www.dcs.gla.ac.uk\/Keith\/Preface.html (1979)."},{"key":"28755_CR52","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.16061","author":"DMW Powers","year":"2020","unstructured":"Powers, D. M. W. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. https:\/\/doi.org\/10.48550\/arXiv.2010.16061 (2020).","journal-title":"Preprint at."},{"key":"28755_CR53","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. & Zisserman, A. The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 303\u2013338 (2010).","journal-title":"Int. J. Comput. Vis."},{"key":"28755_CR54","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Cao, J., You, Y. & Qiao, Y. DenSe-AdViT: A novel Vision Transformer for Dense SAR Object Detection. https:\/\/doi.org\/10.48550\/arXiv.2504.13638 (2025).","DOI":"10.48550\/arXiv.2504.13638"},{"key":"28755_CR55","doi-asserted-by":"publisher","unstructured":"Li, Y., Mao, H., Girshick, R. & He, K. Exploring Plain Vision Transformer Backbones for Object Detection. https:\/\/doi.org\/10.48550\/arXiv.2203.16527 (2022).","DOI":"10.48550\/arXiv.2203.16527"},{"key":"28755_CR56","doi-asserted-by":"publisher","unstructured":"Liu, Z. et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https:\/\/doi.org\/10.48550\/arXiv.2103.14030 (2021).","DOI":"10.48550\/arXiv.2103.14030"},{"key":"28755_CR57","doi-asserted-by":"publisher","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X. & Han, J. Oriented R-CNN for Object Detection. in IEEE\/CVF International Conference on Computer Vision (ICCV) 3500\u20133509https:\/\/doi.org\/10.1109\/ICCV48922.2021.00350 (2021).","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"28755_CR58","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.isprsjprs.2024.10.030","volume":"218","author":"A Pulella","year":"2024","unstructured":"Pulella, A., Sica, F., Lopez, V., Anglberger, C., H\u00e4nsch, R. & H. & Generalization in deep learning-based aircraft classification for SAR imagery. ISPRS J. Photogrammetry Remote Sens. 218, 312\u2013323 (2024).","journal-title":"ISPRS J. Photogrammetry Remote Sens."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-28755-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-28755-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-28755-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:04:09Z","timestamp":1777403049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-28755-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["28755"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-28755-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"21 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"45405"}}