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Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

Author

Listed:
  • Xiangjin Ran

    (College of Earth Science, Jilin University, Changchun 130061, China
    College of Applied Technology, Jilin University, Changchun 130012, China)

  • Linfu Xue

    (College of Earth Science, Jilin University, Changchun 130061, China)

  • Yanyan Zhang

    (Jilin Business and Technology College, Changchun 130012, China)

  • Zeyu Liu

    (College of Earth Science, Jilin University, Changchun 130061, China)

  • Xuejia Sang

    (School of Environment Science and Spatial Informatics (CESI), China University of Mining and Technology, Xuzhou 221008, China)

  • Jinxin He

    (College of Earth Science, Jilin University, Changchun 130061, China)

Abstract

The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.

Suggested Citation

  • Xiangjin Ran & Linfu Xue & Yanyan Zhang & Zeyu Liu & Xuejia Sang & Jinxin He, 2019. "Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network," Mathematics, MDPI, vol. 7(8), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:8:p:755-:d:258747
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    Citations

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    Cited by:

    1. Changcai Yang & Zixuan Teng & Caixia Dong & Yaohai Lin & Riqing Chen & Jian Wang, 2022. "In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images," Agriculture, MDPI, vol. 12(9), pages 1-11, September.
    2. Xiangjin Ran & Linfu Xue & Xuejia Sang & Yao Pei & Yanyan Zhang, 2022. "Intelligent Generation of Cross Sections Using a Conditional Generative Adversarial Network and Application to Regional 3D Geological Modeling," Mathematics, MDPI, vol. 10(24), pages 1-17, December.

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