Journal of Optoelectronics · Laser, Volume. 35, Issue 9, 971(2024)

Hyperspectral image classification based on three-dimensional dilated convolution and graph convolution

LYU Huanhuan1,2, BAI Shuang2, and ZHANG Hui1
Author Affiliations
  • 1School of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000, China
  • 2College of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
  • show less
    References(10)

    [1] [1] WEI W, ZHANG L, LI Y, et al. Intraclass similarity structure representation-based hyperspectral imagery classification with few samples[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:1045-1054.

    [2] [2] TAHMASBIAN I, MORGAN N K, HOSSEINI B, et al. Comparison of hyperspectral imaging and near-infrared spectroscopy to determine nitrogen and carbon concentrations in wheat[J]. Remote Sensing, 2021, 13(6):1128.

    [3] [3] ZHOU B, LI H, XU F. Analysis and discrimination of hyperspectral characteristics of typical vegetation leaves in a rare earth reclamation mining area[J]. Ecological Engineering, 2022, 174:106465.

    [4] [4] ZHANG D, ZHANG J, WANG Z, et al. Tongue colour and coating prediction in traditional Chinese medicine based on visible hyperspectral imaging[J]. IET Image Processing, 2019, 13(12):2265-2270.

    [5] [5] QIAN S E. Hyperspectral satellites, evolution, and development history[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:7032-7056.

    [8] [8] LIU X, SUN Q, MENG Y, et al. Feature extraction and classification of hyperspectral image based on 3D-convolution neural network[C]//2018 IEEE 7th Data Driven Control and Learning Systems Conference, May 25-27, 2018, Enshi, China. New York: IEEE, 2018:918-922.

    [9] [9] ROY S K, KRISHNA G, DUBEY S R, et al. HybridSN: Exploring 3D-2D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(2):277-281.

    [13] [13] HEIDARI N, IOSIFIDIS A. Progressive graph convolutional networks for semi-supervised node classification[J]. IEEE Access, 2021, 9:81957-81968.

    [14] [14] WAN S, GONG C, ZHONG P, et al. Multiscale dynamic graph convolutional network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(5):3162-3177.

    [16] [16] LIU Q, XIAO L, YANG J, et al. CNN-enhanced graph convolutional network with pixel- and superpixel-level feature fusion for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(10):8657-8671.

    Tools

    Get Citation

    Copy Citation Text

    LYU Huanhuan, BAI Shuang, ZHANG Hui. Hyperspectral image classification based on three-dimensional dilated convolution and graph convolution[J]. Journal of Optoelectronics · Laser, 2024, 35(9): 971

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Jan. 29, 2023

    Accepted: Dec. 20, 2024

    Published Online: Dec. 20, 2024

    The Author Email:

    DOI:10.16136/j.joel.2024.09.0020

    Topics