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Sichuan Province","award":["42301473"],"award-info":[{"award-number":["42301473"]}]},{"name":"Natural Science Foundation of Sichuan Province","award":["BX20230299"],"award-info":[{"award-number":["BX20230299"]}]},{"name":"Natural Science Foundation of Sichuan Province","award":["2023M742884"],"award-info":[{"award-number":["2023M742884"]}]},{"name":"Natural Science Foundation of Sichuan Province","award":["2022NSFSC1031"],"award-info":[{"award-number":["2022NSFSC1031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vision transformers (ViTs) are increasingly utilized for HSI classification due to their outstanding performance. However, ViTs encounter challenges in capturing global dependencies among objects of varying sizes, and fail to effectively exploit the spatial\u2013spectral information inherent in HSI. In response to this limitation, we propose a novel solution: the multi-scale spatial\u2013spectral transformer (MSST). Within the MSST framework, we introduce a spatial\u2013spectral token generator (SSTG) and a token fusion self-attention (TFSA) module. Serving as the feature extractor for the MSST, the SSTG incorporates a dual-branch multi-dimensional convolutional structure, enabling the extraction of semantic characteristics that encompass spatial\u2013spectral information from HSI and subsequently tokenizing them. TFSA is a multi-head attention module with the ability to encode attention to features across various scales. We integrated TFSA with cross-covariance attention (CCA) to construct the transformer encoder (TE) for the MSST. Utilizing this TE to perform attention modeling on tokens derived from the SSTG, the network effectively simulates global dependencies among multi-scale features in the data, concurrently making optimal use of spatial\u2013spectral information in HSI. Finally, the output of the TE is fed into a linear mapping layer to obtain the classification results. Experiments conducted on three popular public datasets demonstrate that the MSST method achieved higher classification accuracy compared to state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3390\/rs16020404","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T06:49:31Z","timestamp":1705906171000},"page":"404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Spatial\u2013Spectral Transformer for Hyperspectral Image Classification Based on Global Dependencies of Multi-Scale Features"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunxuan","family":"Ma","sequence":"first","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yan","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yakun","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610097, China"}]},{"given":"Lanxin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Statistics, East China Normal University, Shanghai 200062, China"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yusong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xiaoai","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"ref_1","unstructured":"Srivastava, P.K., Malhi, R.K.M., Pandey, P.C., Anand, A., Singh, P., Pandey, M.K., and Gupta, A. 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