{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T12:31:49Z","timestamp":1783341109731,"version":"3.54.6"},"reference-count":56,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62001434"],"award-info":[{"award-number":["62001434"]}]},{"name":"National Natural Science Foundation of China","award":["62071084"],"award-info":[{"award-number":["62071084"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, deep-learning-based hyperspectral image (HSI) classification networks have become one of the most dominant implementations in HSI classification tasks. Among these networks, convolutional neural networks (CNNs) and attention-based networks have prevailed over other HSI classification networks. While convolutional neural networks with perceptual fields can effectively extract local features in the spatial dimension of HSI, they are poor at capturing the global and sequential features of spectral\u2013spatial information; networks based on attention mechanisms, for example, Transformer, usually have better ability to capture global features, but are relatively weak in discriminating local features. This paper proposes a fusion network of convolution and Transformer for HSI classification, known as FusionNet, in which convolution and Transformer are fused in both serial and parallel mechanisms to achieve the full utilization of HSI features. Experimental results demonstrate that the proposed network has superior classification results compared to previous similar networks, and performs relatively well even on a small amount of training data.<\/jats:p>","DOI":"10.3390\/rs14164066","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["FusionNet: A Convolution\u2013Transformer Fusion Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Liming","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9940-4125","authenticated-orcid":false,"given":"Jinghui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology (NJUST), Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianrui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. 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