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Research Funds for the Central Universities","award":["GZC20233336"],"award-info":[{"award-number":["GZC20233336"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2023M730425"],"award-info":[{"award-number":["2023M730425"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2023CDJXY-037"],"award-info":[{"award-number":["2023CDJXY-037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Several deep learning and transformer models have been recommended in previous research to deal with the classification of hyperspectral images (HSIs). Among them, one of the most innovative is the bidirectional encoder representation from transformers (BERT), which applies a distance-independent approach to capture the global dependency among all pixels in a selected region. However, this model does not consider the local spatial-spectral and spectral sequential relations. In this paper, a dual-dimensional (i.e., spatial and spectral) BERT (the so-called D2BERT) is proposed, which improves the existing BERT model by capturing more global and local dependencies between sequential spectral bands regardless of distance. In the proposed model, two BERT branches work in parallel to investigate relations among pixels and spectral bands, respectively. In addition, the layer intermediate information is used for supervision during the training phase to enhance the performance. We used two widely employed datasets for our experimental analysis. The proposed D2BERT shows superior classification accuracy and computational efficiency with respect to some state-of-the-art neural networks and the previously developed BERT model for this task.<\/jats:p>","DOI":"10.3390\/rs16030539","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:56:34Z","timestamp":1706694994000},"page":"539","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Spatial-Spectral BERT for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0105-3406","authenticated-orcid":false,"given":"Mahmood","family":"Ashraf","sequence":"first","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Xichuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-0638","authenticated-orcid":false,"given":"Gemine","family":"Vivone","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Methodologies for Environmental Analysis (CNR-IMAA), 85050 Tito, Italy"},{"name":"NBFC\u2014National Biodiversity Future Center, 90133 Palermo, Italy"}]},{"given":"Lihui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Rong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"given":"Reza Seifi","family":"Majdard","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ardabil Branch, Islamic Azad University, Ardabil 1477893855, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Edge-inferring graph neural network with dynamic task-guided self-diagnosis for few-shot hyperspectral image classification","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. 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