{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:04:21Z","timestamp":1776081861871,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:00:00Z","timestamp":1644019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, the deep learning-based hyperspectral image (HSI) classification method has achieved great success, and the convolutional neural network (CNN) method has achieved good classification performance in the HSI classification task. However, the convolutional operation only works with local neighborhoods, and is effective in extracting local features. It is difficult to capture interactive features over long distances, which affects the accuracy of classification to some extent. At the same time, the data from HSI have the characteristics of three-dimensionality, redundancy, and noise. To solve these problems, we propose a 3D self-attention multiscale feature fusion network (3DSA-MFN) that integrates 3D multi-head self-attention. 3DSA-MFN first uses different sized convolution kernels to extract multiscale features, samples the different granularities of the feature map, and effectively fuses the spatial and spectral features of the feature map. Then, we propose an improved 3D multi-head self-attention mechanism that provides local feature details for the self-attention branch, and fully exploits the context of the input matrix. To verify the performance of the proposed method, we compare it with six current methods on three public datasets. The experimental results show that the proposed 3DSA-MFN achieves competitive classification and highlights the HSI classification task.<\/jats:p>","DOI":"10.3390\/rs14030742","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Multiscale Feature Fusion Network Incorporating 3D Self-Attention for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuhao","family":"Qing","sequence":"first","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}]},{"given":"Quanzhen","family":"Huang","sequence":"additional","affiliation":[{"name":"Henan Institute of Engineering, School of Electrical Information Engineering, Zhengzhou 451191, China"}]},{"given":"Liuyan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}]},{"given":"Yueyan","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}]},{"given":"Wenyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Instrument and Electronics, North University of China, Taiyuan 030051, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, K., Cheng, T., Deng, X., Yao, X., Tian, Y., Zhu, Y., and Cao, W. 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