Laser & Optoelectronics Progress, Volume. 61, Issue 12, 1228002(2024)

Semantic Segmentation of Remote Sensing Imagery Based on Improved Squeeze and Excitaion Block

Shengwei Wu1, Jiaoli Fang2、*, and Daming Zhu1
Author Affiliations
  • 1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650000, Yunnan , China
  • 2Computer Center, Kunming University of Science and Technology, Kunming 650000, Yunnan , China
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    References(33)

    [11] Vaswani A, Shazeer N, Parmar N et al. Attention is all You need[C], 6000-6010(2017).

    [16] Liu W X, Shu Y Z, Tang X M et al. Remote sensing image segmentation using dual attention mechanism DeepLabV3+ algorithm[J]. Tropical Geography, 40, 303-313(2020).

    [24] Jin S, Guan M, Bian Y C et al. Building extraction from remote sensing images based on improved U-Net[J]. Laser & Optoelectronics Progress, 60, 0401002(2023).

    [26] Su Z P, Li J W, Jiang J W et al. Semantic segmentation method for remote sensing images based on improved DeepLabV3+[J]. Laser & Optoelectronics Progress, 60, 0628003(2023).

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    Shengwei Wu, Jiaoli Fang, Daming Zhu. Semantic Segmentation of Remote Sensing Imagery Based on Improved Squeeze and Excitaion Block[J]. Laser & Optoelectronics Progress, 2024, 61(12): 1228002

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jun. 13, 2023

    Accepted: Aug. 10, 2023

    Published Online: May. 20, 2024

    The Author Email: Jiaoli Fang (fangjiaoli@163.com)

    DOI:10.3788/LOP231528

    CSTR:32186.14.LOP231528

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