{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T03:47:46Z","timestamp":1772164066891,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective. However, RGB-based networks do not take full advantage of the spectral information in hyperspectral data. This inspired the creation of HyperKon, a dedicated hyperspectral Convolutional Neural Network backbone built with self-supervised contrastive representation learning. HyperKon uniquely leverages the high spectral continuity, range, and resolution of hyperspectral data through a spectral attention mechanism. We also perform a thorough ablation study on different kinds of layers, showing their performance in understanding hyperspectral layers. Notably, HyperKon achieves a remarkable 98% Top-1 retrieval accuracy and surpasses traditional RGB-trained backbones in both pansharpening and image classification tasks. These results highlight the potential of hyperspectral-native backbones and herald a paradigm shift in hyperspectral image analysis.<\/jats:p>","DOI":"10.3390\/rs16183399","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T11:04:33Z","timestamp":1726139073000},"page":"3399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0306-4758","authenticated-orcid":false,"given":"Daniel La\u2019ah","family":"Ayuba","sequence":"first","affiliation":[{"name":"Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey GU2 7XH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-5505","authenticated-orcid":false,"given":"Jean-Yves","family":"Guillemaut","sequence":"additional","affiliation":[{"name":"Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey GU2 7XH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7519-1399","authenticated-orcid":false,"given":"Belen","family":"Marti-Cardona","sequence":"additional","affiliation":[{"name":"Centre for Environmental Health and Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4904-4349","authenticated-orcid":false,"given":"Oscar","family":"Mendez","sequence":"additional","affiliation":[{"name":"Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey GU2 7XH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cheng, C., and Zhao, B. (2019). Prospect of application of hyperspectral imaging technology in public security. Proceedings of the International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018: Applications and Techniques in Cyber Security and Intelligence, Springer.","DOI":"10.1007\/978-3-319-98776-7_31"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1080\/07038992.1998.10855254","article-title":"Precision agriculture and the role of remote sensing: A review","volume":"24","author":"Brisco","year":"1998","journal-title":"Can. J. Remote. Sens."},{"key":"ref_3","unstructured":"da Lomba Magalh\u00e3es, M.J. (2022). Hyperspectral Image Fusion\u2014A Comprehensive Review. [Master\u2019s Thesis, University of Eastern Finland]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shi, C., Sun, J., and Wang, L. (2022). Hyperspectral image classification based on spectral multiscale convolutional neural network. Remote. Sens., 14.","DOI":"10.3390\/rs14081951"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107281","DOI":"10.1016\/j.infsof.2023.107281","article-title":"Robustness assessment of hyperspectral image CNNs using metamorphic testing","volume":"162","author":"Bouchoucha","year":"2023","journal-title":"Inf. Softw. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote. Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Feng, F., Wang, S., Wang, C., and Zhang, J. (2019). Learning deep hierarchical spatial\u2013spectral features for hyperspectral image classification based on residual 3D-2D CNN. Sensors, 19.","DOI":"10.3390\/s19235276"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/JSTARS.2020.3011992","article-title":"3-D channel and spatial attention based multiscale spatial\u2013spectral residual network for hyperspectral image classification","volume":"13","author":"Lu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, C., Qiu, Z., Cao, X., Chen, Z., Gao, H., and Hua, Z. (2021). Hybrid dilated convolution with multi-scale residual fusion network for hyperspectral image classification. Micromachines, 12.","DOI":"10.3390\/mi12050545"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gbodjo, Y.J.E., Ienco, D., Leroux, L., Interdonato, R., Gaetano, R., and Ndao, B. (2020). Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships. Remote. Sens., 12.","DOI":"10.3390\/rs12172814"},{"key":"ref_13","first-page":"5622519","article-title":"TransUNetCD: A hybrid transformer network for change detection in optical remote-sensing images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 23\u201328). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1109\/TPAMI.2024.3362475","article-title":"SpectralGPT: Spectral remote sensing foundation model","volume":"46","author":"Hong","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Manas, O., Lacoste, A., Gir\u00f3-i Nieto, X., Vazquez, D., and Rodriguez, P. (2021, January 11\u201317). Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00928"},{"key":"ref_18","first-page":"5502117","article-title":"Foundation model-based multimodal remote sensing data classification","volume":"62","author":"He","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, X., Lao, J., Dang, B., Zhang, Y., Yu, L., Ru, L., Zhong, L., Huang, Z., Wu, K., and Hu, D. (2024, January 17\u201324). Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52733.2024.02613"},{"key":"ref_20","first-page":"1","article-title":"RingMo-SAM: A foundation model for segment anything in multimodal remote-sensing images","volume":"61","author":"Yan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dong, H., Ma, W., Wu, Y., Zhang, J., and Jiao, L. (2020). Self-supervised representation learning for remote sensing image change detection based on temporal prediction. Remote. Sens., 12.","DOI":"10.3390\/rs12111868"},{"key":"ref_22","first-page":"1","article-title":"Hyperspectral imagery classification based on contrastive learning","volume":"60","author":"Hou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_23","first-page":"1","article-title":"Spectral\u2013spatial masked transformer with supervised and contrastive learning for hyperspectral image classification","volume":"61","author":"Huang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, T., Zhou, T., Liu, Y., and Peng, Y. (2021). Contrastive learning based on transformer for hyperspectral image classification. Appl. Sci., 11.","DOI":"10.3390\/app11188670"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nalepa, J., Myller, M., Cwiek, M., Zak, L., Lakota, T., Tulczyjew, L., and Kawulok, M. (2021). Towards on-board hyperspectral satellite image segmentation: Understanding robustness of deep learning through simulating acquisition conditions. Remote. Sens., 13.","DOI":"10.3390\/rs13081532"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113632","DOI":"10.1016\/j.rse.2023.113632","article-title":"The EnMAP imaging spectroscopy mission towards operations","volume":"294","author":"Storch","year":"2023","journal-title":"Remote. Sens. Environment"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3139292","article-title":"Hyperspectral pansharpening based on improved deep image prior and residual reconstruction","volume":"60","author":"Bandara","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_28","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 23\u201329). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, PMLR, Honolulu, HI, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, P., Cui, Z., Gan, Z., and Liu, F. (2020). Three-dimensional resnext network using feature fusion and label smoothing for hyperspectral image classification. Sensors, 20.","DOI":"10.3390\/s20061652"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26\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_31","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_32","unstructured":"Oord, A.v.d., Li, Y., and Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107090","DOI":"10.1016\/j.knosys.2021.107090","article-title":"Review on self-supervised image recognition using deep neural networks","volume":"224","author":"Ohri","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_35","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"33","author":"Caron","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","article-title":"Contrastive representation learning: A framework and review","volume":"8","author":"Healy","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","first-page":"3407","article-title":"Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases","volume":"33","author":"Purushwalkam","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","unstructured":"Robinson, J., Chuang, C.Y., Sra, S., and Jegelka, S. (2020). Contrastive learning with hard negative samples. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/MGRS.2018.2793873","article-title":"Discriminant analysis-based dimension reduction for hyperspectral image classification: A survey of the most recent advances and an experimental comparison of different techniques","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1080\/01431161.2020.1736732","article-title":"Feature extraction for hyperspectral image classification: A review","volume":"41","author":"Kumar","year":"2020","journal-title":"Int. J. Remote. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1080\/10095020.2020.1720529","article-title":"Review on graph learning for dimensionality reduction of hyperspectral image","volume":"23","author":"Zhang","year":"2020","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TGRS.2003.815999","article-title":"Overview of the earth observing one (EO-1) mission","volume":"41","author":"Ungar","year":"2003","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_44","unstructured":"Yokoya, N., and Iwasaki, A. (2016). Airborne Hyperspectral Data over Chikusei, University Tokyo. Tecnical Report SAL-2016-05-27."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8059","DOI":"10.1109\/TGRS.2020.2986313","article-title":"Hyperspectral pansharpening using deep prior and dual attention residual network","volume":"58","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.5194\/isprsarchives-XL-8-1221-2014","article-title":"Quality metrics evaluation of hyperspectral images","volume":"40","author":"Singh","year":"2014","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3224","DOI":"10.1109\/JSTARS.2015.2403257","article-title":"A comprehensive evaluation of spectral distance functions and metrics for hyperspectral image processing","volume":"8","author":"Deborah","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chaithra, C., Taranath, N., Darshan, L., and Subbaraya, C. (2018, January 29\u201331). A Survey on Image Fusion Techniques and Performance Metrics. Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, Coimbatore, India.","DOI":"10.1109\/ICECA.2018.8474818"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1109\/JSTARS.2019.2917584","article-title":"HyperPNN: Hyperspectral pansharpening via spectrally predictive convolutional neural networks","volume":"12","author":"He","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhang, J., Zhao, Z., Sun, K., Liu, J., and Zhang, C. (2021, January 21\u201325). Deep gradient projection networks for pan-sharpening. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00142"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., and Patel, V.M. (2022, January 18\u201324). HyperTransformer: A textural and spectral feature fusion transformer for pansharpening. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00181"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/TGRS.2014.2375320","article-title":"A convex formulation for hyperspectral image superresolution via subspace-based regularization","volume":"53","author":"Simoes","year":"2014","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., and Paisley, J. (2017, January 22\u201329). PanNet: A deep network architecture for pan-sharpening. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.193"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lee, J., Seo, S., and Kim, M. (2021, January 21\u201325). Sipsa-net: Shift-invariant pan sharpening with moving object alignment for satellite imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01003"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging Spectroscopy and the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote. Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","article-title":"Spectral\u2013spatial attention network for hyperspectral image classification","volume":"58","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013spatial residual network for hyperspectral image classification: A 3-D deep learning framework","volume":"56","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Heo, B., Yun, S., Han, D., Chun, S., Choe, J., and Oh, S.J. (2021, January 11\u201317). Rethinking spatial dimensions of vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"ref_59","first-page":"1","article-title":"Hyperspectral image transformer classification networks","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Spectral\u2013spatial feature tokenization transformer for hyperspectral image classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, Y., and Zhou, Y. (2023, January 17\u201324). Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00957"},{"key":"ref_62","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ayush, K., Uzkent, B., Meng, C., Tanmay, K., Burke, M., Lobell, D., and Ermon, S. (2021, January 11\u201317). Geography-aware self-supervised learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01002"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"7724","DOI":"10.1109\/JSTARS.2022.3204541","article-title":"A hyperspectral image change detection framework with self-supervised contrastive learning pretrained model","volume":"15","author":"Ou","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","article-title":"Pruning and quantization for deep neural network acceleration: A survey","volume":"461","author":"Liang","year":"2021","journal-title":"Neurocomputing"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3399\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:55:04Z","timestamp":1760111704000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3399"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":66,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183399"],"URL":"https:\/\/doi.org\/10.3390\/rs16183399","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]}}}