Laser Technology, Volume. 44, Issue 4, 485(2020)

Hyperspectral image classification based on 3-D convolutional recurrent neural network

GUAN Shihao, YANG Guang, LI Hao, and FU Yanyu
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  • [in Chinese]
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    References(20)

    [1] [1] BIOUCAS-DIAS J M, PLAZA A, CAMPS-VALLS G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2):6-36.

    [2] [2] DALE L M, THEWIS A, BOUDRY C, et al. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review[J]. Applied Spectroscopy Reviews, 2013, 48(2):142-159.

    [3] [3] GHIYAMAT A, SHAFRI H Z M. A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment[J]. International Journal of Remote Sensing, 2010, 31(7):1837-1856.

    [4] [4] van der MEER F D, van dwe WERFF H M A, van RUITENBEEK F J A, et al. Multi- and hyperspectral geologic remote sensing: A review[J]. International Journal of Applied Earth Observation & Geoinformation, 2012, 14(1):112-128.

    [5] [5] ELIZABETH A W,SHAROLYN A,MICHAIL F,et al. Supporting global environmental change research: A review of trends and know-ledge gaps in urban remote sensing[J]. Remote Sensing, 2014, 6(5):3879-3905.

    [6] [6] YUEN P W, RICHARDSON M. An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition[J]. The Imaging Science Journal, 2010, 58(5):241-253.

    [7] [7] PILORGET C, BIBRING J P. Automated algorithms to identify and locate grains of specific composition for NIR hyperspectral microscopes: Application to the micromega instrument onboard exomars[J]. Planetary and Space Science, 2014, 99:7-18.

    [8] [8] HU W, HUANG Y Y, WEI L, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015(10): 1-12.

    [9] [9] YANG J, ZHAO Y Q, CHAN C W. Learning and transferring deep joint spectral-spatial features for hyperspectral classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(8):4729-4742.

    [10] [10] HE M, LI B, CHEN H. Multi-scale 3-D deep convolutional neural network for hyperspectral image classification[C]//2017 IEEE International Conference on Image Processing (ICIP). New York,USA:IEEE, 2017:57-61.

    [11] [11] LI G D, ZHANG Ch J, GAO F, et al. Doubleconvpool-structured 3D-CNN for hyperspectral remote sensing image classification[J]. Journal of Image and Graphics, 2019, 24(4): 639-654(in Chin-ese).

    [12] [12] WU H, SAURABH P. Convolutional recurrent neural networks for hyperspectral data classification[J]. Remote Sensing, 2017, 9(3): 298-303.

    [13] [13] MOU L, GHAMISI P, ZHU X X. Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(1):391-406.

    [14] [14] MOU L, GHAMISI P, ZHU X X. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transaction Geoscience and Remote Sensing, 2017, 55(7):3639-3655.

    [15] [15] ZHANG B. Hyperspectral image classification and target detection[M]. Beijing: Science Press, 2011: 9-10(in Chinese).

    [16] [16] DU P J, XIA J Sh, XUE Zh H, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2): 236-256(in Chinese).

    [17] [17] QI Y F, MA Zh Y. Hyperspectral image classification method based on neighborhood speetra and probability cooperative representation[J].Laser Technology, 2019,43(4):448-452(in Chinese).

    [18] [18] ZHANG H K, LI Y, JIANG Y N. Deep learning for hyperspectral imagery classification: The state of the art and prospects[J]. Acta Automatia Sinica, 2018, 44(6): 961-977(in Chinese).

    [19] [19] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014(9):34-37.

    [20] [20] LIU J. Hyperspectral image classification based on long short term memory network[D]. Xi’an:Xidian University, 2018: 19-21(in Chinese).

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    GUAN Shihao, YANG Guang, LI Hao, FU Yanyu. Hyperspectral image classification based on 3-D convolutional recurrent neural network[J]. Laser Technology, 2020, 44(4): 485

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    Paper Information

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    Received: Aug. 12, 2019

    Accepted: --

    Published Online: Jul. 16, 2020

    The Author Email:

    DOI:10.7510/jgjs.issn.1001-3806.2020.04.015

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