{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:42:58Z","timestamp":1772775778737,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2022 Doctoral Research Initiation Fund of Hunan University of Chinese Medicine","award":["0001036"],"award-info":[{"award-number":["0001036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The unique spatial\u2013spectral integration characteristics of hyperspectral imagery (HSI) make it widely applicable in many fields. The spatial\u2013spectral feature fusion-based HSI classification has always been a research hotspot. Typically, classification methods based on spatial\u2013spectral features will select larger neighborhood windows to extract more spatial features for classification. However, this approach can also lead to the problem of non-independent training and testing sets to a certain extent. This paper proposes a spatial shuffle strategy that selects a smaller neighborhood window and randomly shuffles the pixels within the window. This strategy simulates the potential patterns of the pixel distribution in the real world as much as possible. Then, the samples of a three-dimensional HSI cube is transformed into two-dimensional images. Training with a simple CNN model that is not optimized for architecture can still achieve very high classification accuracy, indicating that the proposed method of this paper has considerable performance-improvement potential. The experimental results also indicate that the smaller neighborhood windows can achieve the same, or even better, classification performance compared to larger neighborhood windows.<\/jats:p>","DOI":"10.3390\/rs15163960","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:24:47Z","timestamp":1691663087000},"page":"3960","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Hyperspectral Image Classification via Spatial Shuffle-Based Convolutional Neural Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhihui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China"}]},{"given":"Baisong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7280-1443","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gwon, Y., Kim, D., You, H.J., Nam, S.H., and Kim, Y.D. 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