{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:55:51Z","timestamp":1770990951787,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T00:00:00Z","timestamp":1716681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3072022CF0801"],"award-info":[{"award-number":["3072022CF0801"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018YFE0206500"],"award-info":[{"award-number":["2018YFE0206500"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["614210202030217"],"award-info":[{"award-number":["614210202030217"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["3072022CF0801"],"award-info":[{"award-number":["3072022CF0801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2018YFE0206500"],"award-info":[{"award-number":["2018YFE0206500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["614210202030217"],"award-info":[{"award-number":["614210202030217"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Laboratory of Communication Anti Jamming Technology","award":["3072022CF0801"],"award-info":[{"award-number":["3072022CF0801"]}]},{"name":"National Key Laboratory of Communication Anti Jamming Technology","award":["2018YFE0206500"],"award-info":[{"award-number":["2018YFE0206500"]}]},{"name":"National Key Laboratory of Communication Anti Jamming Technology","award":["614210202030217"],"award-info":[{"award-number":["614210202030217"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a novel adaptive learnable spectral\u2013spatial fusion transformer (ALSST) to enhance HSI classification. The model incorporates a dual-branch adaptive spectral\u2013spatial fusion gating mechanism (ASSF), which captures spectral\u2013spatial fusion features effectively from images. The ASSF comprises two key components: the point depthwise attention module (PDWA) for spectral feature extraction and the asymmetric depthwise attention module (ADWA) for spatial feature extraction. The model efficiently obtains spectral\u2013spatial fusion features by multiplying the outputs of these two branches. Furthermore, we integrate the layer scale and DropKey into the traditional transformer encoder and multi-head self-attention (MHSA) to form a new transformer with a layer scale and DropKey (LD-Former). This innovation enhances data dynamics and mitigates performance degradation in deeper encoder layers. The experiments detailed in this article are executed on four renowned datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and the University of Pavia (UP). The findings demonstrate that the ALSST model secures optimal performance, surpassing some existing models. The overall accuracy (OA) is 99.70%, 89.72%, 97.84%, and 99.78% on four famous datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and University of Pavia (UP), respectively.<\/jats:p>","DOI":"10.3390\/rs16111912","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:36:22Z","timestamp":1716798982000},"page":"1912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Adaptive Learnable Spectral\u2013Spatial Fusion Transformer for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5749-3115","authenticated-orcid":false,"given":"Minhui","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Ship Communication and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yaxiu","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Ship Communication and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Jianhong","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Ship Communication and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Rui","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Ship Communication and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yu","family":"Zhong","sequence":"additional","affiliation":[{"name":"Agile and Intelligent Computing Key Laboratory of Sichuan Province, Chengdu 610000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Czaja, W., Kavalerov, I., and Li, W. 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