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In HSRSI semantics segmentation, the traditional dataset partition method may cause information leakage, which poses challenges for a fair comparison between models. The performance of the model based on \u201cconvolutional-pooling-fully connected\u201d structure is limited by small sample sizes and high dimensions of HSRSI. Moreover, most current studies did not involve how to choose the number of principal components with the application of the principal component analysis (PCA) to reduce dimensionality. To overcome the above challenges, firstly, the non-overlapping sliding window strategy combined with the judgment mechanism is introduced, used to split the hyperspectral dataset. Then, a PSE-UNet model for HSRSI semantic segmentation is designed by combining PCA, the attention mechanism, and UNet, and the factors affecting the performance of PSE-UNet are analyzed. Finally, the cumulative variance contribution rate (CVCR) is introduced as a dimensionality reduction metric of PCA to study the Hughes phenomenon. The experimental results with the Salinas dataset show that the PSE-UNet is superior to other semantic segmentation algorithms and the results can provide a reference for HSRSI semantic segmentation.<\/jats:p>","DOI":"10.3390\/s22249678","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T05:10:19Z","timestamp":1670821819000},"page":"9678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4447-338X","authenticated-orcid":false,"given":"Jiaju","family":"Li","sequence":"first","affiliation":[{"name":"School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2713-0756","authenticated-orcid":false,"given":"Hefeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Natural Resources and Spatial Information, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anbing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Natural Resources and Spatial Information, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Hydrogeology Team of Hebei Coalfield Geology Bureau, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep Learning Classifiers for Hyperspectral Imaging: A Review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. 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