{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T15:23:11Z","timestamp":1776525791315,"version":"3.51.2"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T00:00:00Z","timestamp":1737849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["31660137"],"award-info":[{"award-number":["31660137"]}]},{"name":"National Natural Science Foundation of China","award":["52105283"],"award-info":[{"award-number":["52105283"]}]},{"name":"National Natural Science Foundation of China","award":["2023KT31"],"award-info":[{"award-number":["2023KT31"]}]},{"name":"Huzhou Science and Technology Commissioner Fund","award":["31660137"],"award-info":[{"award-number":["31660137"]}]},{"name":"Huzhou Science and Technology Commissioner Fund","award":["52105283"],"award-info":[{"award-number":["52105283"]}]},{"name":"Huzhou Science and Technology Commissioner Fund","award":["2023KT31"],"award-info":[{"award-number":["2023KT31"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitations when processing hyperspectral remote sensing images, primarily in the following aspects. First, the complex background in the images. Various elements in the background may have similar spectral characteristics to the crops, and this spectral similarity makes the classification model susceptible to background interference, thus reducing classification accuracy. Second, the differences in crop scales increase the difficulty of feature extraction. In different image regions, the scale of crops can vary significantly, and traditional classification methods often struggle to effectively capture this information. Additionally, due to the limitations of spectral information, especially under multi-scale variation backgrounds, the extraction of crop information becomes even more challenging, leading to instability in the classification results. To address these issues, a semantic-guided transformer network (SGTN) is proposed, which aims to effectively overcome the limitations of these deep learning methods and improve crop classification accuracy and robustness. First, a multi-scale spatial\u2013spectral information extraction (MSIE) module is designed that effectively handle the variations of crops at different scales in the image, thereby extracting richer and more accurate features, and reducing the impact of scale changes. Second, a semantic-guided attention (SGA) module is proposed, which enhances the model\u2019s sensitivity to crop semantic information, further reducing background interference and improving the accuracy of crop area recognition. By combining the MSIE and SGA modules, the SGTN can focus on the semantic features of crops at multiple scales, thus generating more accurate classification results. Finally, a two-stage feature extraction structure is employed to further optimize the extraction of crop semantic features and enhance classification accuracy. The results show that on the Indian Pines, Pavia University, and Salinas benchmark datasets, the overall accuracies of the proposed model are 98.24%, 98.34%, and 97.89%, respectively. Compared with other methods, the model achieves better classification accuracy and generalization performance. In the future, the SGTN is expected to be applied to more agricultural remote sensing tasks, such as crop disease detection and yield prediction, providing more reliable technical support for precision agriculture and agricultural monitoring.<\/jats:p>","DOI":"10.3390\/jimaging11020037","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T07:46:29Z","timestamp":1737963989000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Semantic-Guided Transformer Network for Crop Classification in Hyperspectral Images"],"prefix":"10.3390","volume":"11","author":[{"given":"Weiqiang","family":"Pi","sequence":"first","affiliation":[{"name":"College of Intelligent Manufacturing and Elevator, Huzhou Vocational and Technical College, Huzhou 313099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6643-013X","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6806-0876","authenticated-orcid":false,"given":"Rongyang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing and Elevator, Huzhou Vocational and Technical College, Huzhou 313099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guowei","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing and Elevator, Huzhou Vocational and Technical College, Huzhou 313099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing and Elevator, Huzhou Vocational and Technical College, Huzhou 313099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianmin","family":"Du","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109838","DOI":"10.1016\/j.compag.2024.109838","article-title":"An efficient and precise dynamic neighbor graph network for crop mapping using unmanned aerial vehicle hyperspectral imagery","volume":"230","author":"Zhang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80941","DOI":"10.1007\/s11042-024-18562-9","article-title":"An extensive review of hyperspectral image classification and prediction: Techniques and challenges","volume":"83","author":"Tejasree","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Papadopoulos, S., Koukiou, G., and Anastassopoulos, V. (2024). Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification\u2014A Review. J. Imaging, 10.","DOI":"10.3390\/jimaging10010015"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"014512","DOI":"10.1117\/1.JRS.18.014512","article-title":"Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers","volume":"18","author":"Yadav","year":"2024","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107285","DOI":"10.1016\/j.jfranklin.2024.107285","article-title":"Class incremental learning with analytic learning for hyperspectral image classification","volume":"361","author":"Zhuang","year":"2024","journal-title":"J. Frankl. Inst."},{"key":"ref_6","first-page":"21","article-title":"Spectral\u2013Spatial Adaptive Weighted Fusion and Residual Dense Network for hyperspectral image classification","volume":"28","author":"Sun","year":"2025","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101852","DOI":"10.1016\/j.ecoinf.2022.101852","article-title":"Classification of desert grassland species based on a local-global feature enhancement network and UAV hyperspectral remote sensing","volume":"72","author":"Tao","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1007\/s10812-023-01489-8","article-title":"Research on Grassland Rodent Infestation Monitoring Methods Based on Dense Residual Networks and Unmanned Aerial Vehicle Remote Sensing","volume":"89","author":"Zhang","year":"2023","journal-title":"J. Appl. Spectrosc."},{"key":"ref_9","first-page":"1729","article-title":"DNNBoT: Deep Neural Network-Based Botnet Detection and Classification","volume":"71","author":"Verma","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s12524-020-01265-7","article-title":"Spatial\u2013Spectral Image Classification with Edge Preserving Method","volume":"49","author":"Merugu","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2330979","DOI":"10.1080\/22797254.2024.2330979","article-title":"A hybrid convolution transformer for hyperspectral image classification","volume":"57","author":"Arshad","year":"2024","journal-title":"Eur. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1016\/j.asr.2024.08.049","article-title":"A Multiscale Dilated Attention Network for Hyperspectral Image Classification","volume":"74","author":"Tu","year":"2024","journal-title":"Adv. Space Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"016509","DOI":"10.1117\/1.JRS.18.016509","article-title":"Center-similarity spectral-spatial attention network for hyperspectral image classification","volume":"18","author":"Zhang","year":"2024","journal-title":"J. Appl. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, M., Duan, Y., Song, W., Mei, H., and He, Q. (2023). An Effective Hyperspectral Image Classification Network Based on Multi-Head Self-Attention and Spectral-Coordinate Attention. J. Imaging, 9.","DOI":"10.3390\/jimaging9070141"},{"key":"ref_15","unstructured":"Merugu, S., Jain, K., Mittal, A., and Raman, B. (2021). Sub-scene Target Detection and Recognition Using Deep Learning Convolution Neural Networks. ICDSMLA 2019, Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications, Hyderabad, India, 29\u201330 March 2019, Springer."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"108897","DOI":"10.1016\/j.compeleceng.2023.108897","article-title":"Enhanced hyperspectral image segmentation and classification using K-means clustering with connectedness theorem and swarm intelligent-BiLSTM","volume":"110","author":"Christilda","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, T., Bi, Y., Zhu, X., and Gao, X. (2023). Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery. Sensors, 23.","DOI":"10.3390\/s23052856"},{"key":"ref_19","first-page":"036507","article-title":"Robust dense graph structure based on graph convolutional network for hyperspectral image classification","volume":"18","author":"Li","year":"2024","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","first-page":"50","article-title":"Research on Micro-patch Identification of Desert Grassland Based on UAV Remote Sensing","volume":"40","author":"Zhang","year":"2022","journal-title":"J. Guangxi Norm. Univ. Nat. Sci. Ed."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e00363","DOI":"10.1016\/j.ohx.2022.e00363","article-title":"A CNN-based image detector for plant leaf diseases classification","volume":"12","author":"Falaschetti","year":"2022","journal-title":"HardwareX"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"120828","DOI":"10.1016\/j.eswa.2023.120828","article-title":"Local aggregation and global attention network for hyperspectral image classification with spectral-induced aligned superpixel segmentation","volume":"232","author":"Chen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"044525","DOI":"10.1117\/1.JRS.16.044525","article-title":"Transformer attention network and unmanned aerial vehicle hyperspectral remote sensing for grassland rodent pest monitoring research","volume":"16","author":"Zhang","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1080\/01431161.2024.2326042","article-title":"Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery","volume":"45","author":"Zhang","year":"2024","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","first-page":"5528715","article-title":"Hyperspectral Image Transformer Classification Networks","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"6014205","article-title":"Convolution Transformer Mixer for Hyperspectral Image Classification","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","first-page":"155","article-title":"Hyperspectral Image Classification Based on Swin Transformer and 3D Residual Multilayer Fusion Network","volume":"50","author":"Wang","year":"2023","journal-title":"Comput. Sci."},{"key":"ref_29","first-page":"2237","article-title":"Convolutional Neural Network and Vision Transformer-driven Cross-layer Multi-scale Fusion Network for Hyperspectral Image Classification","volume":"46","author":"Zhao","year":"2024","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1016\/j.asr.2024.05.064","article-title":"Dual-stream spectral-spatial convolutional neural network for hyperspectral image classification and optimal band selection","volume":"74","author":"Atik","year":"2024","journal-title":"Adv. Space Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral Image Classification with Deep Feature Fusion Network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Song, C., Mei, S., Ma, M., Xu, F., Zhang, Y., and Du, Q. (2022, January 17\u201322). Hyperspectral Image Classification Using Hierarchical Spatial-Spectral Transformer. Proceedings of the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884329"},{"key":"ref_34","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":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"5516415","article-title":"MASSFormer: Memory-Augmented Spectral-Spatial Transformer for Hyperspectral Image Classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5508105","DOI":"10.1109\/LGRS.2024.3431538","article-title":"Multiscale Super Token Transformer for Hyperspectral Image Classification","volume":"21","author":"Meng","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/37\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:36:23Z","timestamp":1759919783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,26]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["jimaging11020037"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11020037","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,26]]}}}