Laser & Optoelectronics Progress, Volume. 59, Issue 2, 0210014(2022)

Hyperspectral Image Classification Based on Modified DenseNet and Spatial Spectrum Attention Mechanism

Xin Wang* and Yanguo Fan
Author Affiliations
  • College of Oceanography and Spatial Information, China University of Petroleum (East China), Qingdao , Shandong 266500, China
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    References(18)

    [3] Li S H. Hyperspectral image classification based on deep learning[D], 7-15(2019).

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    [13] Zheng S S, Liu W, Shan R et al. A hyperspectral image classification method based on improved multi-scale three-dimensional residual network[J]. Computer Engineering, 46, 215-221(2020).

    [14] Zhang Y P, Zhang C M, Bai J. DenseNet-Attention for hyperspectral remote sensing image classification[J]. Journal of Graphics, 41, 897-904(2020).

    [15] Ma Y J, Liu P P. Convolutional neural network based on DenseNet evolution for image classification algorithm[J]. Laser & Optoelectronics Progress, 57, 241001(2020).

    [16] Peng Y F, Mei J Y, Wang K X et al. Remote sensing image retrieval based on regional attention mechanism[J]. Laser & Optoelectronics Progress, 57, 101017(2020).

    [18] Cheng W J, Chen W Q. Hyperspectral image classification based on MCFFN-Attention[J]. Computer Engineering and Applications, 56, 201-206(2020).

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    Xin Wang, Yanguo Fan. Hyperspectral Image Classification Based on Modified DenseNet and Spatial Spectrum Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0210014

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    Paper Information

    Category: Image Processing

    Received: Jan. 21, 2021

    Accepted: Mar. 15, 2021

    Published Online: Dec. 23, 2021

    The Author Email: Wang Xin (3166588225@qq.com)

    DOI:10.3788/LOP202259.0210014

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