{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:56:55Z","timestamp":1765961815645,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2045"],"award-info":[{"award-number":["U22A2045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral variability and the limited labeled samples create challenges for HSI classification, which results in poor data separability and makes it difficult to learn highly discriminative semantic features. In order to address the above problems, a novel spectral-spatial center-aware bottleneck Transformer is proposed. First, the highly relevant spectral information and the complementary spatial information at different scales are integrated to reduce the impact caused by the high spectral variability and enhance the HSI\u2019s separability. Then, the feature correction layer is designed to model the cross-channel interactions, thereby promoting the effective cooperation between different channels to enhance overall feature representation capability. Finally, the center-aware self-attention is constructed to model the spatial long-range interactions and focus more on the neighboring pixels that have relatively consistent spectral-spatial properties with the central pixel. Experimental results on the common datasets show that compared with the state-of-the-art classification methods, S2CABT has the better classification performance and robustness, which achieves a good compromise between the complexity and the performance.<\/jats:p>","DOI":"10.3390\/rs16122152","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T10:41:03Z","timestamp":1718275263000},"page":"2152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spectral-Spatial Center-Aware Bottleneck Transformer for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2529-8654","authenticated-orcid":false,"given":"Meng","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Sixian","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Pengbo","family":"Mi","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Deqiang","family":"Han","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, L., Liu, H., and Sun, Y. (2024). Hyperspectral image classification with the orthogonal self-attention ResNet and two-step support vector machine. Remote Sens., 16.","DOI":"10.3390\/rs16061010"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, J., Qin, J., Qian, J., Li, A., and Wang, L. (2024). AL-MRIS: An active learning-based multipath residual involution siamese network for few-shot hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16060990"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, H., and Liu, W. (2024). S3L: Spectrum Transformer for self-supervised learning in hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16060970"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cui, B., Wen, J., Song, X., and He, J. (2023). MADANet: A lightweight hyperspectral image classification network with multiscale feature aggregation and a dual attention mechanism. Remote Sens., 15.","DOI":"10.3390\/rs15215222"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Diao, Q., Dai, Y., Wang, J., Feng, X., Pan, F., and Zhang, C. (2024). Spatial-pooling-based graph attention U-Net for hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16060937"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121032","DOI":"10.1016\/j.eswa.2023.121032","article-title":"Multiple vision architectures-based hybrid network for hyperspectral image classification","volume":"234","author":"Zhao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Islam, T., Islam, R., Uddin, P., and Ulhaq, A. (2024). Spectrally segmented-enhanced neural network for precise land cover object classification in hyperspectral imagery. Remote Sens., 16.","DOI":"10.3390\/rs16050807"},{"key":"ref_8","first-page":"5503417","article-title":"A positive feedback spatial-spectral correlation network based on spectral slice for hyperspectral image classification","volume":"61","author":"Shi","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5504520","DOI":"10.1109\/TGRS.2024.3351486","article-title":"A dual branch multiscale Transformer network for hyperspectral image classification","volume":"62","author":"Shi","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, H., Jiang, C., and Feng, J. (2024). Spectral\u2013spatial graph convolutional network with dynamic-synchronized multiscale features for few-shot hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16050895"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Haut, J., Paoletti, M., Paz-Gallardo, A., Plaza, J., Plaza, A., and Vigo-Aguiar, J. (2017, January 4\u20138). Cloud implementation of logistic regression for hyperspectral image classification. Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE), Costa Ballena (Rota), C\u00e1diz, Spain."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, T., Zhang, J., and Zhang, Y. (2014, January 27\u201330). Classification of hyperspectral image based on deep belief networks. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026039"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ma, A., Filippi, A., Wang, Z., and Yin, Z. (2019). Hyperspectral image classification using similarity measurements-based deep recurrent neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11020194"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Seydgar, M., Alizadeh Naeini, A., Zhang, M., Li, W., and Satari, M. (2019). 3-D convolution-recurrent networks for spectral-spatial classification of hyperspectral images. Remote Sens., 11.","DOI":"10.3390\/rs11070883"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5511118","DOI":"10.1109\/TGRS.2023.3272639","article-title":"AAtt-CNN: Automatical attention-based convolutional neural networks for hyperspectral image classification","volume":"61","author":"Paoletti","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, Y., Tian, S., and Ge, J. (2023). Hybrid convolutional network combining multiscale 3D depthwise separable convolution and CBAM residual dilated convolution for hyperspectral image classification. Remote Sens., 15.","DOI":"10.3390\/rs15194796"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, S., Liang, L., Zhang, S., Zhang, Y., Plaza, A., and Wang, X. (2024). End-to-end convolutional network and spectral-spatial Transformer architecture for hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16020325"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fang, S., Li, X., Tian, S., Chen, W., and Zhang, E. (2024). Multi-level feature extraction networks for hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16030590"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Gao, D., Liu, D., and Shi, G. (2024). Spectral-spatial domain attention network for hyperspectral image few-shot classification. Remote Sens., 16.","DOI":"10.3390\/rs16030592"},{"key":"ref_26","first-page":"5508614","article-title":"Attention multi-hop graph and multi-scale convolutional fusion network for hyperspectral image classification","volume":"61","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective kernel networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, Y., Lan, Y., Xie, Y., Yu, L., Chen, C., Wu, Y., and Dai, X. (2024). A Spatial\u2013Spectral Transformer for Hyperspectral Image Classification Based on Global Dependencies of Multi-Scale Features. Remote Sens., 16.","DOI":"10.3390\/rs16020404"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5504605","DOI":"10.1109\/LGRS.2024.3379509","article-title":"Hierarchical attention transformer for hyperspectral image classification","volume":"21","author":"Arshad","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, S., and Zhang, W. (2024). Dilated spectral\u2013spatial Gaussian Transformer net for hyperspectral image classification. Remote Sens., 16.","DOI":"10.3390\/rs16020287"},{"key":"ref_34","first-page":"5502305","article-title":"Nonlocal correntropy matrix representation for hyperspectral image classification","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1109\/JSTARS.2013.2282161","article-title":"A two-stage feature selection framework for hyperspectral image classification using few labeled samples","volume":"7","author":"Jia","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1109\/LGRS.2019.2958833","article-title":"Band grouping SuperPCA for feature extraction and extended morphological profile production from hyperspectral images","volume":"17","author":"Beirami","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5513814","DOI":"10.1109\/TGRS.2023.3281511","article-title":"Multi-attention joint convolution feature representation with lightweight Transformer for hyperspectral image classification","volume":"61","author":"Fang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5508805","DOI":"10.1109\/LGRS.2023.3310572","article-title":"Quaternion convolutional neural network with EMAP representation for multisource remote sensing data classification","volume":"20","author":"Wei","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7412","DOI":"10.1109\/JSTARS.2023.3301721","article-title":"Spatial-spectral feature extraction with local covariance matrix from hyperspectral images through hybrid parallelization","volume":"16","author":"Torti","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","article-title":"3-D deep learning approach for remote sensing image classification","volume":"56","author":"Hamida","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"5500413","article-title":"Feature-grouped network with spectral\u2013spatial connected attention for hyperspectral image classification","volume":"60","author":"Guo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","first-page":"5501916","article-title":"Feedback attention-based dense CNN for hyperspectral image classification","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_47","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":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","first-page":"5507714","article-title":"Spectral partitioning residual network with spatial attention mechanism for hyperspectral image classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8180","DOI":"10.1109\/JSTARS.2021.3103176","article-title":"A multiscale dual-branch feature fusion and attention network for hyperspectral images classification","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual spectral\u2013spatial attention network for hyperspectral image classification","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7831","DOI":"10.1109\/TGRS.2020.3043267","article-title":"Attention-based adaptive spectral\u2013spatial kernel ResNet for hyperspectral image classification","volume":"59","author":"Roy","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/JSTARS.2022.3225928","article-title":"Spatial\u2013spectral split attention residual network for hyperspectral image classification","volume":"16","author":"Shu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5522214","DOI":"10.1109\/TGRS.2022.3221534","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_55","doi-asserted-by":"crossref","first-page":"5515216","DOI":"10.1109\/TGRS.2023.3286950","article-title":"A spectral\u2013spatial fusion Transformer network for hyperspectral image classification","volume":"61","author":"Liao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4401118","DOI":"10.1109\/TGRS.2023.3242978","article-title":"When multigranularity meets spatial\u2013spectral attention: A hybrid Transformer for hyperspectral image classification","volume":"61","author":"Ouyang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 20\u201325). Bottleneck Transformers for visual recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5532117","DOI":"10.1109\/TGRS.2022.3185640","article-title":"BS2T: Bottleneck spatial\u2013spectral Transformer for hyperspectral image classification","volume":"60","author":"Song","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.1109\/JSTARS.2023.3342461","article-title":"D 2 S 2 BoT: Dual-dimension spectral-spatial bottleneck Transformer for hyperspectral image classification","volume":"17","author":"Zhang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Duan, C., Yang, Y., and Wang, X. (2020). Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sens., 12.","DOI":"10.20944\/preprints201912.0059.v2"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:58:20Z","timestamp":1760108300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,13]]},"references-count":61,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16122152"],"URL":"https:\/\/doi.org\/10.3390\/rs16122152","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,6,13]]}}}