Chinese Journal of Liquid Crystals and Displays, Volume. 39, Issue 8, 1001(2024)

Remote sensing image land feature segmentation method based on lightweight DeepLabV3+

Jing MA1,2, Zhonghua GUO1,2、*, Zhiqiang MA1, Xiaoyan MA1,2, and Jialong LI1,2
Author Affiliations
  • 1School of Physics and Electronic-Electrical Engineering,Ningxia University,Yinchuan 750021,China
  • 2Ningxia Key Lab on Information Sensing & Intelligent Desert,Ningxia University,Yinchuan 750021,China
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    References(27)

    [2] CHENG R. The extraction of urban public space information from GF-1 image based on deep learning[D](2021).

    [3] GONG X, GUO Z H, CHEN W. Remote sensing image road segmentation based on CA-TransUNet[J]. Computer and Modernization, 112-118(2023).

    [4] JIN Y W. Building recognition and change detection from high-resolution remotely sensed imagery using deep learning[D](2021).

    [7] LI X N. Research on extraction method of forest land resources information from remote sensing images[D](2022).

    [10] ZHANG T W. Research on deep learning-based SAR ship detection and recognition technology[D](2022).

    [23] ZHOU Y, LIU D R. A semantic segmentation method for remote sensing image based on fusion attention mechanism and DenseASPP improved DeeplabV3+[J]. Remote Sensing Information, 38, 85-92(2023).

    [26] HOWARD A G, ZHU M L, CHEN B et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[J/OL](2017).

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    Jing MA, Zhonghua GUO, Zhiqiang MA, Xiaoyan MA, Jialong LI. Remote sensing image land feature segmentation method based on lightweight DeepLabV3+[J]. Chinese Journal of Liquid Crystals and Displays, 2024, 39(8): 1001

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    Paper Information

    Category: Image Segmentation

    Received: Sep. 6, 2023

    Accepted: --

    Published Online: Sep. 27, 2024

    The Author Email: Zhonghua GUO (guozhh@nxu.edu.cn)

    DOI:10.37188/CJLCD.2023-0293

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