{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:18:40Z","timestamp":1779200320364,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"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":["12090020"],"award-info":[{"award-number":["12090020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Hyperspectral image classification (HSIC) involves analyzing high-dimensional data that contain substantial spectral redundancy and spatial noise, which increases the entropy and uncertainty of feature representations. Reducing such redundancy while retaining informative content in spectral\u2013spatial interactions remains a fundamental challenge for building efficient and accurate HSIC models. Traditional deep learning methods often rely on redundant modules or lack sufficient spectral\u2013spatial coupling, limiting their ability to fully exploit the information content of hyperspectral data. To address these challenges, we propose SGFNet, which is a spectral-guided fusion network designed from an information\u2013theoretic perspective to reduce feature redundancy and uncertainty. First, we designed a Spectral-Aware Filtering Module (SAFM) that suppresses noisy spectral components and reduces redundant entropy, encoding the raw pixel-wise spectrum into a compact spectral representation accessible to all encoder blocks. Second, we introduced a Spectral\u2013Spatial Adaptive Fusion (SSAF) module, which strengthens spectral\u2013spatial interactions and enhances the discriminative information in the fused features. Finally, we developed a Spectral Guidance Gated CNN (SGGC), which is a lightweight gated convolutional module that uses spectral guidance to more effectively extract spatial representations while avoiding unnecessary sequence modeling overhead. We conducted extensive experiments on four widely used hyperspectral benchmarks and compared SGFNet with eight state-of-the-art models. The results demonstrate that SGFNet consistently achieves superior performance across multiple metrics. From an information\u2013theoretic perspective, SGFNet implicitly balances redundancy reduction and information preservation, providing an efficient and effective solution for HSIC.<\/jats:p>","DOI":"10.3390\/e27100995","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:43:12Z","timestamp":1758706992000},"page":"995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SGFNet: Redundancy-Reduced Spectral\u2013Spatial Fusion Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"27","author":[{"given":"Boyu","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Innovation and Engineering, Macau University of Science and Technology, Taipa 999078, Macau"},{"name":"School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Cao","sequence":"additional","affiliation":[{"name":"Faculty of Innovation and Engineering, Macau University of Science and Technology, Taipa 999078, Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dexing","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MSP.2013.2282417","article-title":"Signal and Image Processing in Hyperspectral Remote Sensing [From the Guest Editors]","volume":"31","author":"Ma","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"48","DOI":"10.9734\/jeai\/2024\/v46i12290","article-title":"Hyperspectral Imaging of Soil and Crop: A Review","volume":"46","author":"Vairavan","year":"2024","journal-title":"J. Exp. Agric. Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"031501","DOI":"10.1117\/1.JRS.15.031501","article-title":"Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rajabi, R., Zehtabian, A., Singh, K.D., Tabatabaeenejad, A., Ghamisi, P., and Homayouni, S. (2024). Hyperspectral imaging in environmental monitoring and analysis. Front. Environ. Sci., 11.","DOI":"10.3389\/fenvs.2023.1353447"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","article-title":"Hyperspectral image classification\u2014Traditional to deep models: A survey for future prospects","volume":"15","author":"Ahmad","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3854635","DOI":"10.1155\/2022\/3854635","article-title":"Hyperspectral image classification: Potentials, challenges, and future directions","volume":"2022","author":"Datta","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"3878","DOI":"10.1109\/JSTARS.2024.3353551","article-title":"Conventional to deep ensemble methods for hyperspectral image classification: A comprehensive survey","volume":"17","author":"Ullah","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.1109\/JSTARS.2024.3522318","article-title":"UAV hyperspectral remote sensing image classification: A systematic review","volume":"18","author":"Zhang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shaik, R.U., Periasamy, S., and Zeng, W. (2023). Potential assessment of PRISMA hyperspectral imagery for remote sensing applications. Remote Sens., 15.","DOI":"10.3390\/rs15051378"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11220-015-0126-z","article-title":"Spectral\u2013spatial hyperspectral image classification based on KNN","volume":"17","author":"Huang","year":"2016","journal-title":"Sens. Imaging"},{"key":"ref_12","unstructured":"Mercier, G., and Lennon, M. (2003, January 21\u201325). Support vector machines for hyperspectral image classification with spectral-based kernels. Proceedings of the IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (IEEE Cat. No. 03CH37477), Toulouse, France."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Grewal, R., Singh Kasana, S., and Kasana, G. (2023). Machine learning and deep learning techniques for spectral spatial classification of hyperspectral images: A comprehensive survey. Electronics, 12.","DOI":"10.3390\/electronics12030488"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Manian, V., Alfaro-Mej\u00eda, E., and Tokars, R.P. (2022). Hyperspectral image labeling and classification using an ensemble semi-supervised machine learning approach. Sensors, 22.","DOI":"10.3390\/s22041623"},{"key":"ref_16","first-page":"219","article-title":"Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review","volume":"133","author":"Bera","year":"2022","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions. Computation, 11.","DOI":"10.3390\/computation11030052"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TIP.2018.2809606","article-title":"Diverse region-based CNN for hyperspectral image classification","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","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_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1109\/TGRS.2019.2910603","article-title":"Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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_24","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":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.1080\/01431161.2022.2105668","article-title":"SpectralSWIN: A spectral-swin transformer network for hyperspectral image classification","volume":"43","author":"Ayas","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fu, D., Zeng, Y., and Zhao, J. (2025). DFAST: A Differential-Frequency Attention-Based Band Selection Transformer for Hyperspectral Image Classification. Remote Sens., 17.","DOI":"10.3390\/rs17142488"},{"key":"ref_27","unstructured":"Zhang, G., and Abdulla, W. (2025). Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems. arXiv."},{"key":"ref_28","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sun, M., Wang, L., Jiang, S., Cheng, S., and Tang, L. (2025). HyperSMamba: A Lightweight Mamba for Efficient Hyperspectral Image Classification. Remote Sens., 17.","DOI":"10.3390\/rs17122008"},{"key":"ref_30","unstructured":"Wang, X., Wang, S., Ding, Y., Li, Y., Wu, W., Rong, Y., Kong, W., Huang, J., Li, S., and Yang, H. (2024). State space model for new-generation network alternative to transformers: A survey. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhu, Y., Wang, D., Zhang, L., Chen, T., Wang, Z., and Ye, Z. (2024). A survey on visual mamba. Appl. Sci., 14.","DOI":"10.3390\/app14135683"},{"key":"ref_32","unstructured":"Lv, X., Sun, Y., Zhang, K., Qu, S., Zhu, X., Fan, Y., Wu, Y., Hua, E., Long, X., and Ding, N. (2025). Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, W., and Wang, X. (2025, January 10\u201317). Mambaout: Do we really need mamba for vision?. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.00423"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","article-title":"Principal components analysis (PCA)","volume":"19","author":"Ratajczak","year":"1993","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/02564602.2020.1740615","article-title":"PCA-based feature reduction for hyperspectral remote sensing image classification","volume":"38","author":"Uddin","year":"2021","journal-title":"Iete Tech. Rev."},{"key":"ref_36","first-page":"1","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_37","first-page":"1","article-title":"Channel-layer-oriented lightweight spectral\u2013spatial network for hyperspectral image classification","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","first-page":"1","article-title":"Hyperspectral image classification using groupwise separable convolutional vision transformer network","volume":"62","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","first-page":"1","article-title":"A Fast Dynamic Graph Convolutional Network and CNN Parallel Network for Hyperspectral Image Classification","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TIP.2022.3144017","article-title":"Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification","volume":"31","author":"Dong","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","first-page":"1","article-title":"MambaHSI: Spatial\u2013Spectral Mamba for Hyperspectral Image Classification","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"5538817","article-title":"IGroupSS-Mamba: Interval group spatial-spectral mamba for hyperspectral image classification","volume":"62","author":"He","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/10\/995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:48:43Z","timestamp":1760035723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/10\/995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,24]]},"references-count":44,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["e27100995"],"URL":"https:\/\/doi.org\/10.3390\/e27100995","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,24]]}}}