{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:30:26Z","timestamp":1775579426984,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2021YFH0121"],"award-info":[{"award-number":["2021YFH0121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges. Existing methods cannot extract spatial and spectral-channel contextual information in a targeted manner. In this paper, we propose an encoder\u2013decoder network that fuses spatial attention and spectral-channel attention for HSI classification from three public HSI datasets to tackle these issues. In terms of feature information fusion, a multi-source attention mechanism including spatial and spectral-channel attention is proposed to encode the spatial and spectral multi-channels contextual information. Moreover, three fusion strategies are proposed to effectively utilize spatial and spectral-channel attention. They are direct aggregation, aggregation on feature space, and Hadamard product. In terms of network development, an encoder\u2013decoder framework is employed for hyperspectral image classification. The encoder is a hierarchical transformer pipeline that can extract long-range context information. Both shallow local features and rich global semantic information are encoded through hierarchical feature expressions. The decoder consists of suitable upsampling, skip connection, and convolution blocks, which fuse multi-scale features efficiently. Compared with other state-of-the-art methods, our approach has greater performance in hyperspectral image classification.<\/jats:p>","DOI":"10.3390\/rs14091968","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"1968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder\u2013Decoder Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Jun","family":"Sun","sequence":"first","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"},{"name":"Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2737-0821","authenticated-orcid":false,"given":"Junbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuesong","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"},{"name":"Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mantao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinghua","family":"Ou","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"},{"name":"Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1313-9154","authenticated-orcid":false,"given":"Xiaobo","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"},{"name":"Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-534X","authenticated-orcid":false,"given":"Dejun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","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. 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