Laser & Optoelectronics Progress, Volume. 61, Issue 14, 1428002(2024)

Classification of High-Resolution Remote Sensing Image Based on Swin Transformer and Convolutional Neural Network

Xiaoying He1,2,3, Weiming Xu1,2,3、*, Kaixiang Pan1,2,3, Juan Wang1,2,3, and Ziwei Li1,2,3
Author Affiliations
  • 1The Academy of Digital China, Fuzhou University, Fuzhou 350108, Fujian , China
  • 2Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350002, Fujian , China
  • 3National Engineering Research Center of Geospatial Information Technology, Fuzhou University, Fuzhou 350002, Fujian , China
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    References(33)

    [2] Liu J Y, Zhang Z X, Zhang S W et al. Innovation and development of remote sensing-based land use change studies based on Shupeng Chen’s academic thoughts[J]. Journal of Geo-Information Science, 22, 680-687(2020).

    [4] Gao L, Zhou Y. Environmental effect of land use and land cover change in Wuhan City[J]. Transactions of the Chinese Society of Agricultural Engineering, 24, 73-77(2008).

    [14] Li H D, Gao X H, Tang M. Land cover classification for different spatial resolution images from CNN[J]. Remote Sensing Technology and Application, 35, 749-758(2020).

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    Xiaoying He, Weiming Xu, Kaixiang Pan, Juan Wang, Ziwei Li. Classification of High-Resolution Remote Sensing Image Based on Swin Transformer and Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2024, 61(14): 1428002

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Aug. 29, 2023

    Accepted: Nov. 21, 2023

    Published Online: Jul. 8, 2024

    The Author Email: Weiming Xu (xwming2@126.com)

    DOI:10.3788/LOP232003

    CSTR:32186.14.LOP232003

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