{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:34:23Z","timestamp":1768808063030,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC) Fund","award":["62301174"],"award-info":[{"award-number":["62301174"]}]},{"name":"National Natural Science Foundation of China (NSFC) Fund","award":["2024A04J2081"],"award-info":[{"award-number":["2024A04J2081"]}]},{"name":"National Natural Science Foundation of China (NSFC) Fund","award":["69-6239855"],"award-info":[{"award-number":["69-6239855"]}]},{"name":"Guangzhou basic and applied basic research topics","award":["62301174"],"award-info":[{"award-number":["62301174"]}]},{"name":"Guangzhou basic and applied basic research topics","award":["2024A04J2081"],"award-info":[{"award-number":["2024A04J2081"]}]},{"name":"Guangzhou basic and applied basic research topics","award":["69-6239855"],"award-info":[{"award-number":["69-6239855"]}]},{"name":"Research Project of Guangzhou University","award":["62301174"],"award-info":[{"award-number":["62301174"]}]},{"name":"Research Project of Guangzhou University","award":["2024A04J2081"],"award-info":[{"award-number":["2024A04J2081"]}]},{"name":"Research Project of Guangzhou University","award":["69-6239855"],"award-info":[{"award-number":["69-6239855"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss subtle spatial-spectral features. To address these challenges, this paper proposes an innovative hybrid HSI classification method aggregating hierarchical spatial-spectral features from a CNN and long pixel dependencies from a transformer. The proposed aggregation multi-hierarchical feature network (AMHFN) is designed to capture various hierarchical features and long dependencies from HSI, improving classification accuracy and efficiency. The proposed AMHFN consists of three key modules: (a) a Local-Pixel Embedding module (LPEM) for capturing prominent spatial-spectral features; (b) a Multi-Scale Convolutional Extraction (MSCE) module to capture multi-scale local spatial-spectral features and aggregate hierarchical local features; (c) a Multi-Scale Global Extraction (MSGE) module to explore multi-scale global dependencies and integrate multi-scale hierarchical global dependencies. Rigorous experiments on three public hyperspectral image (HSI) datasets demonstrated the superior performance of the proposed AMHFN method.<\/jats:p>","DOI":"10.3390\/rs16183412","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T11:29:59Z","timestamp":1726226999000},"page":"3412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiaofei","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojin","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2015.05.023","article-title":"Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission","volume":"167","author":"Hestir","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/JSTARS.2020.2980576","article-title":"Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images","volume":"13","author":"Sun","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, L., Tong, Q., and Sun, X. (2012, January 4\u20137). The Spectral Crust project\u2014Research on new mineral exploration technology. Proceedings of the 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China.","DOI":"10.1109\/WHISPERS.2012.6874254"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Noor, S.S.M., Michael, K., Marshall, S., Ren, J., Tschannerl, J., and Kao, F. (2016, January 23\u201325). The properties of the cornea based on hyperspectral imaging: Optical biomedical engineering perspective. Proceedings of the 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502710"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/JSTARS.2015.2406339","article-title":"Generation of Spectral\u2013Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications","volume":"8","author":"Gevaert","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"4099","article-title":"Local Manifold Learning-Based k-Nearest-Neighbor for Hyperspectral Image Classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Song, W., Li, S., Kang, X., and Huang, K. (2016, January 10\u201315). Hyperspectral image classification based on KNN sparse representation. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729622"},{"key":"ref_8","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_9","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":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/TGRS.2013.2258676","article-title":"SVM active learning approach for image classification using spatial information","volume":"52","author":"Pasolli","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, S., Jia, X., and Zhang, B. (2013, January 21\u201326). Superpixel-based Markov random field for classification of hyperspectral images. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723581"},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","unstructured":"Ran, L., Zhang, Y., Wei, W., and Yang, T. (2016, January 19\u201321). Bands sensitive convolutional network for hyperspectral image classification. Proceedings of the International Conference on Internet Multimedia Computing and Service, Xi\u2019an, China.","DOI":"10.1145\/3007669.3007707"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4520","DOI":"10.1109\/TGRS.2017.2693346","article-title":"Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks","volume":"55","author":"Mei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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_17","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 18\u201324). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning, Online."},{"key":"ref_18","first-page":"14745","article-title":"Transgan: Two pure transformers can make one strong gan, and that can scale up","volume":"34","author":"Jiang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., J\u00e9gou, H., and Douze, M. (2021, January 11\u201317). Levit: A vision transformer in convnet\u2019s clothing for faster inference. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Online.","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"ref_21","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_22","first-page":"1","article-title":"Hyperspectral image classification using group-aware hierarchical transformer","volume":"60","author":"Mei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., and Van de Walle, R. (2015, January 26\u201330). Hyperspectral image classification with convolutional neural networks. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806306"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource remote sensing data classification based on convolutional neural network","volume":"56","author":"Xu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded recurrent neural networks for hyperspectral image classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-spatial attention networks for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/LGRS.2017.2786272","article-title":"Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network","volume":"15","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2016, January 10\u201315). Fully convolutional neural networks for remote sensing image classification. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730322"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative adversarial networks for hyperspectral image classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised hyperspectral image classification based on generative adversarial networks","volume":"15","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TGRS.2018.2871782","article-title":"Capsule networks for hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","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."},{"key":"ref_35","first-page":"03762","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","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_37","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_38","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_39","first-page":"1","article-title":"Global\u2013local 3-D convolutional transformer network for hyperspectral image classification","volume":"61","author":"Qi","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3229361","article-title":"Local Semantic Feature Aggregation-Based Transformer for Hyperspectral Image Classification","volume":"60","author":"Tu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"1","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_42","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.h., He, H., Zhuo, W., Wen, S., Lee, C.H., and Chan, S.H.G. (2023, January 17\u201324). Run, Don\u2019t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/2150704X.2017.1331053","article-title":"A semi-supervised convolutional neural network for hyperspectral image classification","volume":"8","author":"Liu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_44","unstructured":"Sharma, V., Diba, A., Tuytelaars, T., and Van Gool, L. (2016). Hyperspectral CNN for image classification & band selection, with application to face recognition. Technical Report KUL\/ESAT\/PSI\/1604, KU Leuven, ESAT."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Heo, B., Yun, S., Han, D., Chun, S., Choe, J., and Oh, S.J. (2021, January 10\u201317). Rethinking spatial dimensions of vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01172"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3412\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:55:55Z","timestamp":1760111755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":45,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183412"],"URL":"https:\/\/doi.org\/10.3390\/rs16183412","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]}}}