{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:55:35Z","timestamp":1770990935758,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["42201386"],"award-info":[{"award-number":["42201386"]}]},{"name":"Natural Science Foundation of China","award":["QNXM20220033"],"award-info":[{"award-number":["QNXM20220033"]}]},{"name":"Natural Science Foundation of China","award":["BK20BE014"],"award-info":[{"award-number":["BK20BE014"]}]},{"name":"International Exchange Growth Program for Young Teachers of USTB","award":["42201386"],"award-info":[{"award-number":["42201386"]}]},{"name":"International Exchange Growth Program for Young Teachers of USTB","award":["QNXM20220033"],"award-info":[{"award-number":["QNXM20220033"]}]},{"name":"International Exchange Growth Program for Young Teachers of USTB","award":["BK20BE014"],"award-info":[{"award-number":["BK20BE014"]}]},{"name":"Scientific and Technological Innovation Foundation of Shunde Innovation School, USTB","award":["42201386"],"award-info":[{"award-number":["42201386"]}]},{"name":"Scientific and Technological Innovation Foundation of Shunde Innovation School, USTB","award":["QNXM20220033"],"award-info":[{"award-number":["QNXM20220033"]}]},{"name":"Scientific and Technological Innovation Foundation of Shunde Innovation School, USTB","award":["BK20BE014"],"award-info":[{"award-number":["BK20BE014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral sensors provide an opportunity to capture the intensity of high spatial\/spectral information and enable applications for high-level earth observation missions, such as accurate land cover mapping and target\/object detection. Currently, convolutional neural networks (CNNs) are good at coping with hyperspectral image processing tasks because of the strong spatial and spectral feature extraction ability brought by hierarchical structures, but the convolution operation in CNNs is limited to local feature extraction in both dimensions. In the meanwhile, the introduction of the Transformer structure has provided an opportunity to capture long-distance dependencies between tokens from a global perspective; however, Transformer-based methods have a restricted ability to extract local information because they have no inductive bias, as CNNs do. To make full use of these two methods\u2019 advantages in hyperspectral image processing, a dual-flow architecture named Hyper-LGNet to couple local and global features is firstly proposed by integrating CNN and Transformer branches to deal with HSI spatial-spectral information. In particular, a spatial-spectral feature fusion module (SSFFM) is designed to maximally integrate spectral and spatial information. Three mainstream hyperspectral datasets (Indian Pines, Pavia University and Houston 2013) are utilized to evaluate the proposed method\u2019s performance. Comparative results show that the proposed Hyper-LGNet achieves state-of-the-art performance in comparison with the other nine approaches concerning overall accuracy (OA), average accuracy (AA) and kappa index. Consequently, it is anticipated that, by coupling CNN and Transformer structures, this study can provide novel insights into hyperspectral image analysis.<\/jats:p>","DOI":"10.3390\/rs14205251","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"5251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Tianxiang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China"}]},{"given":"Wenxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2432-0821","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Yuanxiu","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-6708","authenticated-orcid":false,"given":"Zhifang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Jiangyun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"},{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern trends in hyperspectral image analysis: A review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1146\/annurev-phyto-080417-050100","article-title":"Hyperspectral sensors and imaging technologies in phytopathology: State of the art","volume":"56","author":"Mahlein","year":"2018","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.neucom.2021.06.072","article-title":"Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery","volume":"459","author":"Yi","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Su, J., Yi, D., Liu, C., Guo, L., and Chen, W.H. (2017). Dimension reduction aided hyperspectral image classification with a small-sized training dataset: Experimental comparisons. Sensors, 17.","DOI":"10.3390\/s17122726"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","article-title":"A survey: Deep learning for hyperspectral image classification with few labeled samples","volume":"448","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hong, D., Han, Z., Yao, J., Gao, L., Zhang, B., Plaza, A., and Chanussot, J. (2021). SpectralFormer: Rethinking hyperspectral image classification with transformers. arXiv.","DOI":"10.1109\/TGRS.2021.3130716"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1080\/10106049.2019.1629647","article-title":"Land use\/land cover in view of earth observation: Data sources, input dimensions, and classifiers\u2014A review of the state of the art","volume":"36","author":"Pandey","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TGRS.2017.2744662","article-title":"Random forest ensembles and extended multiextinction profiles for hyperspectral image classification","volume":"56","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10419","DOI":"10.1007\/s11042-017-4403-9","article-title":"Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification","volume":"77","author":"Bo","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced spectral classifiers for hyperspectral images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ranjan, S., Nayak, D.R., Kumar, K.S., Dash, R., and Majhi, B. (2017, January 6\u20137). Hyperspectral image classification: A k-means clustering based approach. Proceedings of the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS.2017.8014707"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.1109\/TGRS.2018.2815613","article-title":"Hyperspectral image classification with deep learning models","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"8672","DOI":"10.1109\/TGRS.2021.3053204","article-title":"Iterative training sampling coupled with active learning for semisupervised spectral\u2013spatial hyperspectral image classification","volume":"59","author":"Ma","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TSP.2022.3144954","article-title":"Hyperspectral image classification using adaptive weighted quaternion Zernike moments","volume":"70","author":"Li","year":"2022","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8689","DOI":"10.1109\/JSTARS.2021.3088228","article-title":"Morphological Convolutional Neural Networks for Hyperspectral Image Classification","volume":"14","author":"Roy","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3228927","article-title":"Convolutional Neural Networks for Multimodal Remote Sensing Data Classification","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108224","DOI":"10.1016\/j.patcog.2021.108224","article-title":"Deep neural networks-based relevant latent representation learning for hyperspectral image classification","volume":"121","author":"Sellami","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep learning for classification of hyperspectral data: A comparative review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep learning for remote sensing image classification: A survey","volume":"8","author":"Li","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","first-page":"8065","DOI":"10.1109\/TGRS.2019.2918080","article-title":"Visual attention-driven hyperspectral image classification","volume":"57","author":"Haut","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.isprsjprs.2018.05.014","article-title":"Hyperspectral image classification via a random patches network","volume":"142","author":"Xu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5008","DOI":"10.1109\/TGRS.2020.3024258","article-title":"Fusion of spectral\u2013spatial classifiers for hyperspectral image classification","volume":"59","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"He, M., Li, B., and Chen, H. (2017, January 17\u201320). Multi-scale 3D deep convolutional neural network for hyperspectral image classification. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale dynamic graph convolutional network for hyperspectral image classification","volume":"58","author":"Wan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1109\/JSTARS.2019.2911987","article-title":"Hyperspectral image classification method based on CNN architecture embedding with hashing semantic feature","volume":"12","author":"Yu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","unstructured":"Antoun, W., Baly, F., and Hajj, H. (2020). Arabert: Transformer-based model for arabic language understanding. arXiv."},{"key":"ref_35","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. (2020, January 16\u201320). Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref_36","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 IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers","volume":"58","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, X., Chen, Y., and Lin, Z. (2021). Spatial-Spectral Transformer for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13030498"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1109\/LGRS.2019.2911322","article-title":"Optimized input for CNN-based hyperspectral image classification using spatial transformer network","volume":"16","author":"He","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_41","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., and Xu, Y. (2022). A survey on vision transformer. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). 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_43","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5251\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:23Z","timestamp":1760144303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,20]]},"references-count":43,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205251"],"URL":"https:\/\/doi.org\/10.3390\/rs14205251","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,20]]}}}