Laser Journal, Volume. 45, Issue 7, 157(2024)

Remote sensing laser image feature localization technology based on deep learning

LUO Tong1 and WANG Lanyi2
Author Affiliations
  • 1University of Sanya, School of Information and Intelligent Engineering, Academician Guoliang Chen Team Innovation Center, Sanya Hainan 572022, China
  • 2University of Sanya Institute of Technology, Sanya Hainan 572022, China
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    References(5)

    [1] [1] Zhang Hong, Wu Zhiwei, Wang Jicheng, et al. Unsupervised band selection for hyperspectral image classification using the Wasserstein metric-based configuration entropy[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 405-415.

    [2] [2] Sun Weiwei, Du Qian. Graph-regularized fast and robust principal component analysis for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3185-3195.

    [3] [3] Gao Peichao, Wang Jicheng, Zhang Hong, et al. Boltzmann entropy-based unsupervised band selection for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 462-466.

    [9] [9] Wei Lifei, Yu Ming, Zhong Yanfei, et al. Hyperspectral image classification method based on space-spectral fusion conditional random field[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(3): 343-354.

    [13] [13] Liang Yueji, Ren Chao, Huang Yibang, et al. Multi-star linear regression retrieval model for monitoring soil moisture using GPS-IR[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 833-842.

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    LUO Tong, WANG Lanyi. Remote sensing laser image feature localization technology based on deep learning[J]. Laser Journal, 2024, 45(7): 157

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    Paper Information

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    Received: Nov. 22, 2023

    Accepted: Dec. 20, 2024

    Published Online: Dec. 20, 2024

    The Author Email:

    DOI:10.14016/j.cnki.jgzz.2024.07.157

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