{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:32:52Z","timestamp":1768008772194,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001362"],"award-info":[{"award-number":["42001362"]}],"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>This paper presents the MSSFF (multistage spectral\u2013spatial feature fusion) framework, which introduces a novel approach for semantic segmentation from hyperspectral imagery (HSI). The framework aims to simplify the modeling of spectral relationships in HSI sequences and unify the architecture for semantic segmentation of HSIs. It incorporates a spectral\u2013spatial feature fusion module and a multi-attention mechanism to efficiently extract hyperspectral features. The MSSFF framework reevaluates the potential impact of spectral and spatial features on segmentation models and leverages the spectral\u2013spatial fusion module (SSFM) in the encoder component to effectively extract and enhance these features. Additionally, an efficient Transformer (ET) is introduced in the skip connection part of deep features to capture long-term dependent features and extract global spectral\u2013spatial information from the entire feature map. This highlights the significant potential of Transformers in modeling spectral\u2013spatial feature maps within the context of hyperspectral remote sensing. Moreover, a spatial attention mechanism is adopted in the shallow skip connection part to extract local features. The framework demonstrates promising capabilities in hyperspectral remote sensing applications. The conducted experiments provide valuable insights for optimizing the model depth and the order of feature fusion, thereby contributing to the advancement of hyperspectral semantic segmentation research.<\/jats:p>","DOI":"10.3390\/rs15245717","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T08:55:16Z","timestamp":1702457716000},"page":"5717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MSSFF: Advancing Hyperspectral Classification through Higher-Accuracy Multistage Spectral\u2013Spatial Feature Fusion"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5723-4789","authenticated-orcid":false,"given":"Yuhan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Qingdao Innovation and Development Center (Base), Harbin Engineering University, Qingdao 266000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6693-957X","authenticated-orcid":false,"given":"Qingyun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9622-5041","authenticated-orcid":false,"given":"Weimin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University, St. John\u2019s, NL A1B 3X5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral Imaging for Military and Security Applications: Combining Myriad Processing and Sensing Techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. 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