{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:23:20Z","timestamp":1776216200039,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T00:00:00Z","timestamp":1588032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"1.National Natural Science Foundation of China;2.Jiangsu Provincial Natural Science Foundation of China","award":["1.Grant 61701238, Grant 61772274, Grant 11431015, Grant 61671243;2. Grant BK20170858, Grant BK20180018, Grant BK20191409"],"award-info":[{"award-number":["1.Grant 61701238, Grant 61772274, Grant 11431015, Grant 61671243;2. Grant BK20170858, Grant BK20180018, Grant BK20191409"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral\u2013spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification.<\/jats:p>","DOI":"10.3390\/rs12091395","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T01:29:15Z","timestamp":1588123755000},"page":"1395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Lightweight Spectral\u2013Spatial Feature Extraction and Fusion Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7186-0492","authenticated-orcid":false,"given":"Linlin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science &amp; Technology, Nanjing 210094, China"},{"name":"Zijin College, Nanjing University of Science &amp; Technology, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science &amp; Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science &amp; Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. 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