Laser & Optoelectronics Progress, Volume. 56, Issue 15, 151006(2019)

Hyperspectral Image Classification Based on Residual Dense Network

Xiangpo Wei*, Xuchu Yu, Xiong Tan, and Bing Liu
Author Affiliations
  • Information Engineering University, Zhengzhou, Henan 450001, China
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    References(19)

    [7] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

    [9] Wang C, Liu Y, Bai X et al. Deep residual convolutional neural network for hyperspectral image super-resolution[M]. ∥Zhao Y, Kong X, Taubman D. Image and graphics. ICIG 2017. Lecture notes in computer science. Cham: Springer, 10668, 370-380(2017).

    [11] Zhong J. Research on semi-supervised learning method of remote sensing image based on deep neural network[D]. Shenyang: Shenyang Aerospace University, 38-47(2018).

    [14] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

    [16] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution. [C]∥2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2472-2481(2018).

    [17] Kingma D P. -01-30)[2019-01-05]. https:∥arxiv., org/abs/1412, 6980(2017).

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    Xiangpo Wei, Xuchu Yu, Xiong Tan, Bing Liu. Hyperspectral Image Classification Based on Residual Dense Network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 151006

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    Paper Information

    Category: Image Processing

    Received: Jan. 3, 2019

    Accepted: Mar. 6, 2019

    Published Online: Aug. 5, 2019

    The Author Email: Xiangpo Wei (13526635671@163.com)

    DOI:10.3788/LOP56.151006

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