{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T10:09:39Z","timestamp":1783850979073,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High-level Talents Programme of National University of Defense Technology","award":["12204541"],"award-info":[{"award-number":["12204541"]}]},{"name":"National Natural Science Foundation of China","award":["12204541"],"award-info":[{"award-number":["12204541"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The hyperspectral image (HSI) distinguishes itself in material identification through its exceptional spectral resolution. However, its spatial resolution is constrained by hardware limitations, prompting the evolution of HSI super-resolution (SR) techniques. Single HSI SR endeavors to reconstruct high-spatial-resolution HSI from low-spatial-resolution inputs, and recent progress in deep learning-based algorithms has significantly advanced the quality of reconstructed images. However, convolutional methods struggle to extract comprehensive spatial and spectral features. Transformer-based models have yet to harness long-range dependencies across both dimensions fully, thus inadequately integrating spatial and spectral data. To solve the above problem, in this paper, we propose a new HSI SR method, SSAformer, which merges the strengths of CNNs and Transformers. It introduces specially designed attention mechanisms for HSI, including spatial and spectral attention modules, and overcomes the previous challenges in extracting and amalgamating spatial and spectral information. Evaluations on benchmark datasets show that SSAformer surpasses contemporary methods in enhancing spatial details and preserving spectral accuracy, underscoring its potential to expand HSI\u2019s utility in various domains, such as environmental monitoring and remote sensing.<\/jats:p>","DOI":"10.3390\/rs16101766","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T09:30:03Z","timestamp":1715851803000},"page":"1766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SSAformer: Spatial\u2013Spectral Aggregation Transformer for Hyperspectral Image Super-Resolution"],"prefix":"10.3390","volume":"16","author":[{"given":"Haoqian","family":"Wang","sequence":"first","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5543-504X","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongjie","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangai","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4091-8399","authenticated-orcid":false,"given":"Zhongyang","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5175-0819","authenticated-orcid":false,"given":"Teng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral Imaging for Military and Security Applications: Combining Myriad Processing and Sensing Techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.54097\/ajst.v6i2.9435","article-title":"Review of Hyperspectral Imaging in Environmental Monitoring Progress and Applications","volume":"6","author":"Zhang","year":"2023","journal-title":"Acad. J. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Poojary, N., D\u2019Souza, H., Puttaswamy, M.R., and Kumar, G.H. (2015, January 15\u201317). Automatic target detection in hyperspectral image processing: A review of algorithms. Proceedings of the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China.","DOI":"10.1109\/FSKD.2015.7382255"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2023.3289293","article-title":"Transformer Meets Remote Sensing Video Detection and Tracking: A Comprehensive Survey","volume":"16","author":"Jiao","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.inffus.2022.08.032","article-title":"Multispectral and hyperspectral image fusion in remote sensing: A survey","volume":"89","author":"Vivone","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1109\/JAS.2023.123681","article-title":"Hyperspectral Image Super-Resolution Meets Deep Learning: A Survey and Perspective","volume":"10","author":"Wang","year":"2023","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TGRS.2019.2937901","article-title":"Hyperspectral Image Recovery Using Nonconvex Sparsity and Low-Rank Regularizations","volume":"58","author":"Hu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Bodrito, T., Zouaoui, A., Chanussot, J., and Mairal, J. (2021). A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, M., Sun, X., Zhu, Q., and Zheng, G. (2021, January 11\u201316). A Survey of Hyperspectral Image Super-Resolution Technology. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554409"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, C., Wang, Y., Zhang, N., Zhang, Y., and Zhao, Z. (2023). A Review of Hyperspectral Image Super-Resolution Based on Deep Learning. Remote. Sens., 15.","DOI":"10.3390\/rs15112853"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y.R. (2018). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. arXiv.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TCI.2020.2996075","article-title":"Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery","volume":"6","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_15","first-page":"1","article-title":"MSDformer: Multiscale Deformable Transformer for Hyperspectral Image Super-Resolution","volume":"61","author":"Chen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., and Zhang, G. (2018, January 8\u201314). Multi-scale Residual Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lu, T., Wang, J., Zhang, Y., Wang, Z., and Jiang, J. (2019). Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11131588"},{"key":"ref_18","first-page":"5000905","article-title":"Remote Sensing Image Super-Resolution via Multiscale Enhancement Network","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mei, S., Yuan, X., Ji, J., Zhang, Y., Wan, S., and Du, Q. (2017). Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9111139"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, Q., and Li, X. (2020). Mixed 2D\/3D Convolutional Network for Hyperspectral Image Super-Resolution. Remote Sens., 12.","DOI":"10.3390\/rs12101660"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"591","DOI":"10.14358\/PERS.72.5.591","article-title":"MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery","volume":"72","author":"Aiazzi","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2015, January 1\u20137). Bayesian sparse representation for hyperspectral image super resolution. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298986"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"083641","DOI":"10.1117\/1.JRS.8.083641","article-title":"Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery","volume":"8","author":"Sun","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/JSTARS.2017.2779539","article-title":"Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition","volume":"11","author":"Wang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, L., Tian, C., Ding, C., Zhang, Y., and Wei, W. (2017, January 10\u201314). Hyperspectral image super-resolution extending: An effective fusion based method without knowing the spatial transformation matrix. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019510"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1137\/S0036144593251710","article-title":"On Projection Algorithms for Solving Convex Feasibility Problems","volume":"38","author":"Bauschke","year":"1996","journal-title":"SIAM Rev."},{"key":"ref_27","first-page":"5537617","article-title":"GJTD-LR: A Trainable Grouped Joint Tensor Dictionary With Low-Rank Prior for Single Hyperspectral Image Super-Resolution","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6055","DOI":"10.1109\/TGRS.2019.2904108","article-title":"Hyperspectral Image Super-Resolution Using Deep Feature Matrix Factorization","volume":"57","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TIP.2005.854479","article-title":"Super-resolution reconstruction of hyperspectral images","volume":"14","author":"Akgun","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108280","DOI":"10.1016\/j.patcog.2021.108280","article-title":"Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization","volume":"122","author":"He","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/JSTARS.2017.2655112","article-title":"Hyperspectral Image Superresolution by Transfer Learning","volume":"10","author":"Yuan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, L., Dingl, C., Wei, W., and Zhang, Y. (2018, January 13\u201316). Single Hyperspectral Image Super-Resolution with Grouped Deep Recursive Residual Network. Proceedings of the 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), Xi\u2019an, China.","DOI":"10.1109\/BigMM.2018.8499097"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7806","DOI":"10.1080\/01431161.2018.1471546","article-title":"Hyperspectral image super-resolution with spectral\u2013spatial network","volume":"39","author":"Jia","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models are Few-Shot Learners. arXiv."},{"key":"ref_37","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 3\u20135). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA."},{"key":"ref_38","unstructured":"Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., and Funtowicz, M. (2019, January 29). Transformers: State-of-the-Art Natural Language Processing. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref_40","first-page":"5531715","article-title":"Interactformer: Interactive Transformer and CNN for Hyperspectral Image Super-Resolution","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5543714","DOI":"10.1109\/TGRS.2022.3221550","article-title":"Multilevel Progressive Network With Nonlocal Channel Attention for Hyperspectral Image Super-Resolution","volume":"60","author":"Hu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"126277","DOI":"10.1016\/j.neucom.2023.126277","article-title":"Combining global receptive field and spatial spectral information for single-image hyperspectral super-resolution","volume":"542","author":"Wu","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_43","unstructured":"Geng, Z., Guo, M.H., Chen, H., Li, X., Wei, K., and Lin, Z. (2021). Is Attention Better Than Matrix Decomposition?. arXiv."},{"key":"ref_44","unstructured":"Katharopoulos, A., Vyas, A., Pappas, N., and Fleuret, F. (2020, January 13\u201318). Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, C., Zhang, Q., Guo, J., Gao, X., and Zhang, J. (2023, January 22\u201329). ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.02109"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_48","unstructured":"Chen, X., Wang, X., Zhang, W., Kong, X., Qiao, Y., Zhou, J., and Dong, C. (2023). HAT: Hybrid Attention Transformer for Image Restoration. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_50","first-page":"1","article-title":"Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and Spatial\u2013Spectral Consistency Regularization","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","unstructured":"Yokoya, N., and Iwasaki, A. (2016). Airborne Hyperspectral Data over Chikusei, The University of Tokyo."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/JSTARS.2019.2911113","article-title":"Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest","volume":"12","author":"Xu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1080\/01431160802559046","article-title":"A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, northern Italy","volume":"30","author":"Huang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_55","unstructured":"Yuhas, R.H., Goetz, A.F.H., and Boardman, J.W. (1992). Discrimination among Semi-Arid Landscape Endmembers Using the Spectral Angle Mapper (SAM) Algorithm, NTRS."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","article-title":"Hyperspectral Pansharpening: A Review","volume":"3","author":"Loncan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_57","unstructured":"Wald, L. (2002). Data Fusion. Definitions and Architectures\u2014Fusion of Images of Different Spatial Resolutions, Presses des MINES."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:43:24Z","timestamp":1760107404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,16]]},"references-count":57,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101766"],"URL":"https:\/\/doi.org\/10.3390\/rs16101766","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,16]]}}}