{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:09:01Z","timestamp":1764842941299,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chunhui Program Cooperative Research Project of Chinese Ministry of Education","award":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"],"award-info":[{"award-number":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"]}]},{"name":"Henan Provincial Science and Technology Research Project","award":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"],"award-info":[{"award-number":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"]}]},{"name":"Key Research Project Fund of Institution of Higher Education in Henan Province","award":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"],"award-info":[{"award-number":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"]}]},{"name":"Henan Polytechnic University for the Double First-Class Project of Surveying and Mapping Disciplines","award":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"],"award-info":[{"award-number":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"]}]},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI Grant","award":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"],"award-info":[{"award-number":["HZKY20220279","232102211019","222102210131","23A520029","GCCYJ202413","23K18517"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72%, 5.19%, and 1.94% in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods.<\/jats:p>","DOI":"10.3390\/rs16224202","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T11:34:11Z","timestamp":1731324851000},"page":"4202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6060-790X","authenticated-orcid":false,"given":"Chunyang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"given":"Chao","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"given":"Bibo","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4597-877X","authenticated-orcid":false,"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Environmental Remote Sensing, Chiba University, Chiba 2638522, Japan"}]},{"given":"Yingjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No.129, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3295-8972","authenticated-orcid":false,"given":"Gaige","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}]},{"given":"Zongze","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. 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