{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:47:35Z","timestamp":1776275255910,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62361042"],"award-info":[{"award-number":["62361042"]}]},{"name":"National Natural Science Foundation of China","award":["20225BCJ23019"],"award-info":[{"award-number":["20225BCJ23019"]}]},{"name":"National Natural Science Foundation of China","award":["20224ACB202002"],"award-info":[{"award-number":["20224ACB202002"]}]},{"name":"National Natural Science Foundation of China","award":["20224BAB202007"],"award-info":[{"award-number":["20224BAB202007"]}]},{"name":"National Natural Science Foundation of China","award":["20232BAB202039"],"award-info":[{"award-number":["20232BAB202039"]}]},{"name":"Training Program for Academic and Technical Leaders of Jiangxi Province","award":["62361042"],"award-info":[{"award-number":["62361042"]}]},{"name":"Training Program for Academic and Technical Leaders of Jiangxi Province","award":["20225BCJ23019"],"award-info":[{"award-number":["20225BCJ23019"]}]},{"name":"Training Program for Academic and Technical Leaders of Jiangxi Province","award":["20224ACB202002"],"award-info":[{"award-number":["20224ACB202002"]}]},{"name":"Training Program for Academic and Technical Leaders of Jiangxi Province","award":["20224BAB202007"],"award-info":[{"award-number":["20224BAB202007"]}]},{"name":"Training Program for Academic and Technical Leaders of Jiangxi Province","award":["20232BAB202039"],"award-info":[{"award-number":["20232BAB202039"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["62361042"],"award-info":[{"award-number":["62361042"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20225BCJ23019"],"award-info":[{"award-number":["20225BCJ23019"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20224ACB202002"],"award-info":[{"award-number":["20224ACB202002"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20224BAB202007"],"award-info":[{"award-number":["20224BAB202007"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20232BAB202039"],"award-info":[{"award-number":["20232BAB202039"]}]},{"name":"China Scholarship Council","award":["62361042"],"award-info":[{"award-number":["62361042"]}]},{"name":"China Scholarship Council","award":["20225BCJ23019"],"award-info":[{"award-number":["20225BCJ23019"]}]},{"name":"China Scholarship Council","award":["20224ACB202002"],"award-info":[{"award-number":["20224ACB202002"]}]},{"name":"China Scholarship Council","award":["20224BAB202007"],"award-info":[{"award-number":["20224BAB202007"]}]},{"name":"China Scholarship Council","award":["20232BAB202039"],"award-info":[{"award-number":["20232BAB202039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their limited receptive domain. The transformer can compensate well for this shortcoming, but it suffers from a lack of image-specific inductive biases (i.e., localization and translation equivariance) and contextual position information compared with CNNs. To overcome the aforementioned challenges, we introduce a simply structured, end-to-end convolutional network and spectral\u2013spatial transformer (CNSST) architecture for HSIC. Our CNSST architecture consists of two essential components: a simple 3D-CNN-based hierarchical feature fusion network and a spectral\u2013spatial transformer that introduces inductive bias information. The former employs a 3D-CNN-based hierarchical feature fusion structure to establish the correlation between spectral and spatial (SAS) information while capturing richer inductive bias and more discriminative local spectral-spatial hierarchical feature information, while the latter aims to establish the global dependency among HSI pixels while enhancing the acquisition of local information by introducing inductive bias information. Specifically, the spectral and inductive bias information is incorporated into the transformer\u2019s multi-head self-attention mechanism (MHSA), thus making the attention spectrally aware and location-aware. Furthermore, a Lion optimizer is exploited to boost the classification performance of our newly developed CNSST. Substantial experiments conducted on three publicly accessible hyperspectral datasets unequivocally showcase that our proposed CNSST outperforms other state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/rs16020325","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T11:43:53Z","timestamp":1705059833000},"page":"325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1774-9267","authenticated-orcid":false,"given":"Shiping","family":"Li","sequence":"first","affiliation":[{"name":"School of Materials Science Engineering, Wuhan Institute of Technology, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6958-0443","authenticated-orcid":false,"given":"Lianhui","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"},{"name":"Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Polit\u00e9cnica, University of Extremadura, E-10071 C\u00e1ceres, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1454-9665","authenticated-orcid":false,"given":"Shaoquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Polit\u00e9cnica, University of Extremadura, E-10071 C\u00e1ceres, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0177-6850","authenticated-orcid":false,"given":"Xuehua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Materials Science Engineering, Wuhan Institute of Technology, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. 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