{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T21:16:15Z","timestamp":1783977375763,"version":"3.55.0"},"reference-count":56,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["ASFC-20184370012"],"award-info":[{"award-number":["ASFC-20184370012"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["61474093"],"award-info":[{"award-number":["61474093"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["ASFC-20184370012"],"award-info":[{"award-number":["ASFC-20184370012"]}]},{"name":"National Natural Science Foundation of China","award":["61474093"],"award-info":[{"award-number":["61474093"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar is widely applied to ship detection due to generating high-resolution images under diverse weather conditions and its penetration capabilities, making SAR images a valuable data source. However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives and missed detections, posing challenges for lightweight and high-precision algorithms. There is an urgent need to improve accuracy of algorithms and their deployability. This paper introduces LH-YOLO, a YOLOv8n-based, lightweight, and high-precision SAR ship detection model. We propose a lightweight backbone network, StarNet-nano, and employ element-wise multiplication to construct a lightweight feature extraction module, LFE-C2f, for the neck of LH-YOLO. Additionally, a reused and shared convolutional detection (RSCD) head is designed using a weight sharing mechanism. These enhancements significantly reduce model size and computational demands while maintaining high precision. LH-YOLO features only 1.862 M parameters, representing a 38.1% reduction compared to YOLOv8n. It exhibits a 23.8% reduction in computational load while achieving a mAP50 of 96.6% on the HRSID dataset, which is 1.4% higher than YOLOv8n. Furthermore, it demonstrates strong generalization on the SAR-Ship-Dataset with a mAP50 of 93.8%, surpassing YOLOv8n by 0.7%. LH-YOLO is well-suited for environments with limited resources, such as embedded systems and edge computing platforms.<\/jats:p>","DOI":"10.3390\/rs16224340","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T06:11:54Z","timestamp":1732169514000},"page":"4340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["LH-YOLO: A Lightweight and High-Precision SAR Ship Detection Model Based on the Improved YOLOv8n"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1244-4568","authenticated-orcid":false,"given":"Qi","family":"Cao","sequence":"first","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7276-4275","authenticated-orcid":false,"given":"Yongqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haisheng","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2077-1912","authenticated-orcid":false,"given":"Zhenjiao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9393-6224","authenticated-orcid":false,"given":"Feng","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","first-page":"222","article-title":"Counteracting imagery (IMINT), optoelectronic (EOIMINT) and radar (SAR) intelligence","volume":"54","author":"Wysocki","year":"2022","journal-title":"Sci. J. Mil. Univ. Land Forces"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/isprs-archives-XLII-5-W3-1-2019","article-title":"A comparative assessment of remote sensing imaging techniques: Optical, sar and lidar","volume":"XLII-5\/W3","author":"Agrawal","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, C., Su, H., Gao, L., and Wang, T. (2022). Deep Learning for SAR Ship Detection: Past, Present and Future. Remote Sens., 14.","DOI":"10.3390\/rs14112712"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14353","DOI":"10.1109\/JSTARS.2024.3437187","article-title":"Ship Detection with SAR C-Band Satellite Images: A Systematic Review","volume":"17","author":"Alexandre","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00500-022-07522-w","article-title":"Ship detection based on deep learning using SAR imagery: A systematic literature review","volume":"27","author":"Yasir","year":"2023","journal-title":"Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TGRS.2019.2931353","article-title":"CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Smith, M., and Varshney, P. (1997, January 13\u201315). VI-CFAR: A novel CFAR algorithm based on data variability. Proceedings of the 1997 IEEE National Radar Conference, Syracuse, NY, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/7.18645","article-title":"OS-CFAR theory for multiple targets and nonuniform clutter","volume":"24","author":"Blake","year":"1988","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11760-007-0043-2","article-title":"OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies","volume":"2","author":"Abdou","year":"2008","journal-title":"Signal Image Video Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1109\/LGRS.2016.2605583","article-title":"Mixture-Based Superpixel Segmentation and Classification of SAR Images","volume":"13","author":"Arisoy","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","first-page":"1","article-title":"Revisiting SLIC: Fast Superpixel Segmentation of Marine SAR Images Using Density Features","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5236217","DOI":"10.1109\/TGRS.2022.3213305","article-title":"Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense","volume":"60","author":"Peng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, Q., Zhu, W., Li, Y., Zhu, B., Gao, T., and Wang, P. (2021, January 12\u201314). Survey of Target Detection Algorithms in SAR Images. Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9390728"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11825","DOI":"10.1007\/s10462-023-10455-x","article-title":"Ship detection with deep learning: A survey","volume":"56","author":"Er","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_16","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_17","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Adarsh, P., Rathi, P., and Kumar, M. (2020, January 6\u20137). YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"122212","DOI":"10.1016\/j.eswa.2023.122212","article-title":"E-YOLO: Recognition of estrus cow based on improved YOLOv8n model","volume":"238","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo algorithm developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, S., Zhan, R., Wang, W., and Zhang, J. (2022). LMSD-YOLO: A Lightweight YOLO Algorithm for Multi-Scale SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14194801"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7029","DOI":"10.1109\/JSTARS.2024.3376558","article-title":"DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments","volume":"17","author":"Tang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1109\/JSTARS.2024.3365807","article-title":"YOLO-OSD: Optimized Ship Detection and Localization in Multiresolution SAR Satellite Images Using a Hybrid Data-Model Centric Approach","volume":"17","author":"Humayun","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_25","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 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Hao, Y. (2022). A Survey of SAR Image Target Detection Based on Convolutional Neural Networks. Remote Sens., 14.","DOI":"10.3390\/rs14246240"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feng, Y., Chen, J., Huang, Z., Wan, H., Xia, R., Wu, B., Sun, L., and Xing, M. (2022). A Lightweight Position-Enhanced Anchor-Free Algorithm for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14081908"},{"key":"ref_31","first-page":"104137","article-title":"YOLOShipTracker: Tracking ships in SAR images using lightweight YOLOv8","volume":"134","author":"Yasir","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gao, Z., Yu, X., Rong, X., and Wang, W. (2024). Improved YOLOv8n for Lightweight Ship Detection. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12101774"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TIP.2022.3231126","article-title":"Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery","volume":"32","author":"Wang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., and Feng, J. (November, January 27). PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment. Proceedings of the The IEEE International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, X., Bai, Y., Wang, Y., and Fu, Y. (2024, January 16\u201322). Rewrite the Stars. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00544"},{"key":"ref_39","unstructured":"Zhou, D., Hou, Q., Chen, Y., Feng, J., and Yan, S. (2020, January 23\u201328). Rethinking bottleneck structure for efficient mobile network design. Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK. Part III 16."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_41","first-page":"10353","article-title":"HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions","volume":"Volume 35","author":"Koyejo","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_42","first-page":"21002","article-title":"Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (November, January 27). NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seoul, Republic of Korea.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds. Remote Sens., 11.","DOI":"10.3390\/rs11070765"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, C., Hu, R., and Yu, Y. (2023). ESarDet: An Efficient SAR Ship Detection Method Based on Context Information and Large Effective Receptive Field. Remote Sens., 15.","DOI":"10.20944\/preprints202305.0374.v1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1080\/01431161.2023.2182652","article-title":"Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images","volume":"44","author":"Kong","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ren, X., Bai, Y., Liu, G., and Zhang, P. (2023). YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection. Remote Sens., 15.","DOI":"10.3390\/rs15153771"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"37030","DOI":"10.1109\/ACCESS.2024.3373893","article-title":"SHIP-YOLO: A Lightweight Synthetic Aperture Radar Ship Detection Model Based on YOLOv8n Algorithm","volume":"12","author":"Luo","year":"2024","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, S., and Wang, H. (November, January 30). SAR target recognition based on deep learning. Proceedings of the 2014 International Conference on Data Science and Advanced Analytics (DSAA), Shanghai, China.","DOI":"10.1109\/DSAA.2014.7058124"},{"key":"ref_52","first-page":"364","article-title":"Convolutional Neural Network With Data Augmentation for SAR Target Recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2526213","DOI":"10.1109\/TIM.2024.3436130","article-title":"DACO-BD: Data Augmentation Combinatorial Optimization-Based Backdoor Defense in Deep Neural Networks for SAR Image Classification","volume":"73","author":"Zeng","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1080\/01431161.2024.2343433","article-title":"Scene-aware data augmentation for ship detection in SAR images","volume":"45","author":"Yu","year":"2024","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"13217","DOI":"10.1109\/JSTARS.2024.3426288","article-title":"M-FSDistill: A Feature Map Knowledge Distillation Algorithm for SAR Ship Detection","volume":"17","author":"Wang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","first-page":"5202718","article-title":"SARGap: A Full-Link General Decoupling Automatic Pruning Algorithm for Deep Learning-Based SAR Target Detectors","volume":"62","author":"Yu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:36:17Z","timestamp":1760114177000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"references-count":56,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224340"],"URL":"https:\/\/doi.org\/10.3390\/rs16224340","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,20]]}}}