{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:12:55Z","timestamp":1772298775188,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Star Team of Xi'an University of Posts &amp; Telecommunications","award":["xyt2016-01"],"award-info":[{"award-number":["xyt2016-01"]}]},{"name":"Program of Qingjiang Excellent Young Talents, Jiangxi University of Science and Technology","award":["JXUSTQJYX2020019"],"award-info":[{"award-number":["JXUSTQJYX2020019"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2021JM-461"],"award-info":[{"award-number":["2021JM-461"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071378"],"award-info":[{"award-number":["62071378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901198"],"award-info":[{"award-number":["61901198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071379"],"award-info":[{"award-number":["62071379"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.<\/jats:p>","DOI":"10.3390\/rs13204060","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T21:45:32Z","timestamp":1633988732000},"page":"4060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-2749","authenticated-orcid":false,"given":"Zhe","family":"Meng","sequence":"first","affiliation":[{"name":"School of Telecommunication and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-9573","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Telecommunication and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-7114","authenticated-orcid":false,"given":"Miaomiao","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, GanZhou 341000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tai, X., Li, M., Xiang, M., and Ren, P. 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