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Nonetheless, the high dimensionality of HSI and the limited number of labeled data remain significant obstacles to HSI classification technology. To alleviate the above problems, we propose an attention-embedded triple-branch fusion convolutional neural network (AETF-Net) for an HSI classification. The network consists of a spectral attention branch, a spatial attention branch, and a multi-attention fusion branch (MAFB). The spectral branch introduces the cross-channel attention to alleviate the band redundancy problem in high dimensions, while the spatial branch preserves the location information of features and eliminates interfering image elements by a bi-directional spatial attention module. These pre-extracted spectral and spatial attention features are then embedded into a novel MAFB with large kernel decomposition technique. The proposed AETF-Net achieves multi-attention features reuse and extracts more representative and discriminative features. Experimental results on three well-known datasets demonstrate the superiority of the method AETF-Net.<\/jats:p>","DOI":"10.3390\/rs15082150","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T01:42:39Z","timestamp":1681954959000},"page":"2150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Attention-Embedded Triple-Fusion Branch CNN for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3408-1932","authenticated-orcid":false,"given":"Erlei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]},{"given":"Jiayi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]},{"given":"Jiaxin","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]},{"given":"Jiarong","family":"Bian","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]},{"given":"Shaoyi","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9283-4488","authenticated-orcid":false,"given":"Tao","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9954-0757","authenticated-orcid":false,"given":"Mingchen","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Northwest A&F University, Xi\u2019an 712100, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","article-title":"Hyperspectral Image Classification Traditional to Deep Models: A Survey for Future Prospects","volume":"15","author":"Ahmad","year":"2022","journal-title":"IEEE J. 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