{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:10:19Z","timestamp":1775913019712,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62076137"],"award-info":[{"award-number":["62076137"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the hyperspectral image (HSI) classification task, every HSI pixel is labeled as a specific land cover category. Although convolutional neural network (CNN)-based HSI classification methods have made significant progress in enhancing classification performance in recent years, they still have limitations in acquiring deep semantic features and face the challenges of escalating computational costs with increasing network depth. In contrast, the Transformer framework excels in expressing high-level semantic features. This study introduces a novel classification network by extracting spectral\u2013spatial features with an enhanced Transformer with Large-Kernel Attention (ETLKA). Specifically, it utilizes distinct branches of three-dimensional and two-dimensional convolutional layers to extract more diverse shallow spectral\u2013spatial features. Additionally, a Large-Kernel Attention mechanism is incorporated and applied before the Transformer encoder to enhance feature extraction, augment comprehension of input data, reduce the impact of redundant information, and enhance the model\u2019s robustness. Subsequently, the obtained features are input to the Transformer encoder module for feature representation and learning. Finally, a linear layer is employed to identify the first learnable token for sample label acquisition. Empirical validation confirms the outstanding classification performance of ETLKA, surpassing several advanced techniques currently in use. This research provides a robust and academically rigorous solution for HSI classification tasks, promising significant contributions in practical applications.<\/jats:p>","DOI":"10.3390\/rs16010067","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:48:37Z","timestamp":1703450917000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spectral\u2013Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention"],"prefix":"10.3390","volume":"16","author":[{"given":"Wen","family":"Lu","sequence":"first","affiliation":[{"name":"The College of Computer, Qinghai Normal University, Xining 810000, China"}]},{"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-3800","authenticated-orcid":false,"given":"Yuhui","family":"Zheng","sequence":"additional","affiliation":[{"name":"The College of Computer, Qinghai Normal University, Xining 810000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3330957","article-title":"Model-Guided Coarse-to-Fine Fusion Network for Unsupervised Hyperspectral Image Super-Resolution","volume":"20","author":"Li","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","first-page":"1","article-title":"X-Shaped Interactive Autoencoders with Cross-Modality Mutual Learning for Unsupervised Hyperspectral Image Super-Resolution","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1109\/JSTARS.2022.3175191","article-title":"SPANet: Successive pooling attention network for semantic segmentation of remote sensing images","volume":"15","author":"Sun","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3108965","article-title":"Efficient semantic segmentation of hyperspectral images using adaptable rectangular convolution","volume":"19","author":"Paoletti","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ben-Ahmed, O., Urruty, T., Richard, N., and Fernandez-Maloigne, C. (2019). Toward content-based hyperspectral remote sensing image retrieval (CB-HRSIR): A preliminary study based on spectral sensitivity functions. Remote Sens., 11.","DOI":"10.3390\/rs11050600"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5618314","DOI":"10.1109\/TGRS.2023.3305021","article-title":"CRNet: Channel-enhanced Remodeling-based Network for Salient Object Detection in Optical Remote Sensing Images","volume":"61","author":"Sun","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8257","DOI":"10.1109\/TGRS.2020.3042507","article-title":"Recurrent thrifty attention network for remote sensing scene recognition","volume":"59","author":"Fu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1109\/TAES.2006.314578","article-title":"Automatic target recognition for hyperspectral imagery using high-order statistics","volume":"42","author":"Ren","year":"2006","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MAES.2010.5546306","article-title":"A tutorial overview of anomaly detection in hyperspectral images","volume":"25","author":"Matteoli","year":"2010","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/TNNLS.2020.3038659","article-title":"Prior-based tensor approximation for anomaly detection in hyperspectral imagery","volume":"33","author":"Li","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3514","DOI":"10.1109\/TGRS.2012.2224874","article-title":"A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms","volume":"51","author":"Pedergnana","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1109\/TGRS.2013.2286953","article-title":"Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles","volume":"52","author":"Song","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4768","DOI":"10.1109\/TGRS.2015.2409195","article-title":"Random Subspace Ensembles for Hyperspectral Image Classification with Extended Morphological Attribute Profiles","volume":"53","author":"Xia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kwan, C., Gribben, D., Ayhan, B., Bernabe, S., Plaza, A., and Selva, M. (2020). Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12091392"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kwan, C., Ayhan, B., Budavari, B., Lu, Y., Perez, D., Li, J., Bernabe, S., and Plaza, A. (2020). Deep Learning for Land Cover Classification Using Only a Few Bands. Remote Sens., 12.","DOI":"10.3390\/rs12122000"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, A., Sun, G., Ma, P., Jia, X., Ren, J., Huang, H., and Zhang, X. (2019). Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles. Remote Sens., 11.","DOI":"10.3390\/rs11080952"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8113","DOI":"10.1109\/JSTARS.2021.3103858","article-title":"Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection","volume":"14","author":"Huang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12745","DOI":"10.1109\/TCYB.2021.3088519","article-title":"Multiview learning with robust double-sided twin SVM","volume":"52","author":"Ye","year":"2021","journal-title":"IEEE Trans. Cyber."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/TNNLS.2020.3027588","article-title":"Learning Robust Discriminant Subspace Based on Joint L2, p-and L2, s-Norm Distance Metrics","volume":"33","author":"Fu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3818","DOI":"10.1109\/TNNLS.2019.2944869","article-title":"Nonpeaked discriminant analysis for data representation","volume":"30","author":"Ye","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Baassou, B., He, M., and Mei, S. (2013, January 20\u201322). An accurate SVM-based classification approach for hyperspectral image classification. Proceedings of the 2013 21st International Conference on Geoinformatics, Kaifeng, China.","DOI":"10.1109\/Geoinformatics.2013.6626036"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1109\/TIP.2008.918955","article-title":"Customizing kernel functions for SVM-based hyperspectral image classification","volume":"17","author":"Guo","year":"2008","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-based edge-preserving features for hyperspectral image classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, R., and Wu, Q. (2016, January 21\u201324). Hyperspectral image classification based on PCA network. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071787"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4865","DOI":"10.1109\/TGRS.2011.2153861","article-title":"Hyperspectral image classification with independent component discriminant analysis","volume":"49","author":"Villa","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2008.2005729","article-title":"Classification of hyperspectral images with regularized linear discriminant analysis","volume":"47","author":"Bandos","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6649","DOI":"10.1109\/TCYB.2022.3219855","article-title":"NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image Classification","volume":"53","author":"Su","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going Deeper With Contextual CNN for Hyperspectral Image Classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Multi-structure KELM with attention fusion strategy for hyperspectral image classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1109\/TGRS.2020.3008286","article-title":"Multiscale residual network with mixed depthwise convolution for hyperspectral image classification","volume":"59","author":"Gao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"102687","article-title":"Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples","volume":"107","author":"Gao","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","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_34","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_35","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and transferring deep joint spectral\u2013spatial features for hyperspectral classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_39","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_40","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":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4133","DOI":"10.1109\/JSTARS.2020.3008949","article-title":"A novel cubic convolutional neural network for hyperspectral image classification","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3112755","article-title":"Lightweight heterogeneous kernel convolution for hyperspectral image classification with noisy labels","volume":"19","author":"Roy","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","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_44","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). European Conference on Computer Vision, Springer."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 20\u201325). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., and Schmid, C. (2021, January 20\u201325). Segmenter: Transformer for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"ref_47","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3172371","article-title":"SpectralFormer: Rethinking hyperspectral image classification with transformers","volume":"60","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1109\/JSTARS.2022.3174135","article-title":"Local transformer with spatial partition restore for hyperspectral image classification","volume":"15","author":"Xue","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_52","first-page":"1","article-title":"A 3-d-swin transformer-based hierarchical contrastive learning method for hyperspectral image classification","volume":"60","author":"Huang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","first-page":"1","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_54","first-page":"1","article-title":"Spatial-Spectral 1DSwin Transformer with Group-wise Feature Tokenization for Hyperspectral Image Classification","volume":"61","author":"Xu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","first-page":"1","article-title":"ELS2T: Efficient Lightweight Spectral\u2013Spatial Transformer for Hyperspectral Image Classification","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhu, D., Min, X., and Zhai, G. (2022, January 18\u201322). Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution. Proceedings of the 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan.","DOI":"10.1109\/ICME52920.2022.9859739"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/TCI.2019.2911881","article-title":"Deep Spatial\u2013Spectral Representation Learning for Hyperspectral Image Denoising","volume":"5","author":"Dong","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/LGRS.2020.3013205","article-title":"Unsupervised Feature Learning Using Recurrent Neural Nets for Segmenting Hyperspectral Images","volume":"18","author":"Tulczyjew","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2019.2960945","article-title":"Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders","volume":"17","author":"Nalepa","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, X., Li, T., Meng, H., Cao, X., and Wang, L. (2022). Adversarial Representation Learning for Hyperspectral Image Classification with Small-Sized Labeled Set. Remote Sens., 14.","DOI":"10.3390\/rs14112612"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:41:04Z","timestamp":1760132464000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,23]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010067"],"URL":"https:\/\/doi.org\/10.3390\/rs16010067","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,23]]}}}