Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1810014(2022)

Hyperspectral Image Classification Based on Multi-Scale Feature Fusion Residual Network

Ziqing Deng1, Yang Wang1, Bing Zhang1, Zhao Ding1, Lifeng Bian2, and Chen Yang1、*
Author Affiliations
  • 1Engineering Research Center of Semiconductor Power Device Reliability, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, Guizhou , China
  • 2Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, Jiangsu , China
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    References(19)

    [14] Bi X J, Zhou Z Y. Hyperspectral image classification algorithm based on two-channel generative adversarial network[J]. Acta Optica Sinica, 39, 1028002(2019).

    [15] Yan M, Zhao H D, Li Y H et al. Multi-classification and recognition of hyperspectral remote sensing objects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 021702(2019).

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

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    Ziqing Deng, Yang Wang, Bing Zhang, Zhao Ding, Lifeng Bian, Chen Yang. Hyperspectral Image Classification Based on Multi-Scale Feature Fusion Residual Network[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810014

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    Paper Information

    Category: Image Processing

    Received: Jun. 15, 2021

    Accepted: Aug. 10, 2021

    Published Online: Aug. 29, 2022

    The Author Email: Yang Chen (eliot.c.yang@163.com)

    DOI:10.3788/LOP202259.1810014

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