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

Xiangjin Ran; Linfu Xue; Yanyan Zhang; Zeyu Liu; Xuejia Sang; Jinxin He

doi:10.3390/math7080755

journal article Open Access Aug 18, 2019

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

Xiangjin Ran Linfu Xue

Yanyan Zhang

Zeyu Liu

Xuejia Sang Jinxin He

Mathematics Vol. 7 No. 8 pp. 755 · MDPI AG

View at Publisher Save 10.3390/math7080755

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.

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References

39

[1]

Kunttu "Rock image classification using color features in Gabor space" J. Electron. Imaging (2005) 10.1117/1.2149872

[2]

Chatterjee "Vision-based rock-type classification of limestone using multi-class support vector machine" Appl. Intell. (2013) 10.1007/s10489-012-0391-7

[3]

Lepistö, L., Kunttu, I., Autio, J., and Visa, A. (2004, January 21–23). Rock image retrieval and classification based on granularity. Proceedings of the 5th International Workshop on Image Analysis for Multimedia Interactive Services, Lisboa, Portugal.

[4]

Patel "Computer vision-based limestone rock-type classification using probabilistic neural network" Geosci. Front. (2016) 10.1016/j.gsf.2014.10.005

[5]

Ke "Gesture segmentation based on a two-phase estimation of distribution algorithm" Inf. Sci. (2017)

[6]

Lepistö, L., Kunttu, I., Autio, J., and Visa, A. (2003, January 3–7). Rock image classification using non-homogenous textures and spectral imaging. Proceedings of the 11th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2003 (WSCG 2003), Plzen-Bory, Czech Republic.

[7]

Partio, M., Cramariuc, B., Gabbouj, M., and Visa, A. (2002, January 4–10). Rock texture retrieval using gray level co-occurrence matrix. Proceedings of the 5th Nordic Signal Processing Symposium (NORSIG-2002), Trondheim, Norway.

[8]

Sharma "A patch-based convolutional neural network for remote sensing image classification" Neural Netw. (2017) 10.1016/j.neunet.2017.07.017

[9]

Nogueira "Towards better exploiting convolutional neural networks for remote sensing scene classification" Pattern Recognit. (2017) 10.1016/j.patcog.2016.07.001

[10]

Perez "Rock lithological classification using multi-scale Gabor features from sub-images, and voting with rock contour information" Int. J. Miner. Process. (2015) 10.1016/j.minpro.2015.09.015

[11]

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Bin Liu, Dongjian He, Yuxiang Li

Symmetry 10.3390/sym10010011

[12]

Guo "Deep learning for visual understanding: A review" Neurocomputing (2016) 10.1016/j.neucom.2015.09.116

[13]

Deep learning

Yann LeCun, Yoshua Bengio, Geoffrey Hinton

Nature 2015 10.1038/nature14539

[14]

Krizhevsky "ImageNet classification with deep convolutional neural networks" Commun. ACM (2012)

[15]

Yang, C., Li, W., and Lin, Z. (2018). Vehicle Object Detection in Remote Sensing Imagery Based on Multi-Perspective Convolutional Neural Network. Int. J. Geo-Inf., 7. 10.3390/ijgi7070249

[16]

Han, S., Ren, F., Wu, C., Chen, Y., Du, Q., and Ye, X. (2018). Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data. Int. J. Geo-Inf., 7. 10.3390/ijgi7040158

[17]

Sargano, A., Angelov, P., and Habib, Z. (2017). A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition. Appl. Sci., 7. 10.3390/app7010110

[18]

Sainath "Deep Convolutional Neural Networks for large-scale speech tasks" Neural Netw. Off. J. Int. Neural Netw. Soc. (2015) 10.1016/j.neunet.2014.08.005

[19]

Audio-visual speech recognition using deep learning

Kuniaki Noda, Yuki Yamaguchi, Kazuhiro Nakadai et al.

Applied Intelligence 2015 10.1007/s10489-014-0629-7

[20]

Lv "Traffic Flow Prediction with Big Data: A Deep Learning Approach" IEEE Trans. Intell. Transp. Syst. (2015)

[21]

Sermanet "Traffic sign recognition with multi-scale Convolutional Networks" Int. Jt. Conf. Neural Netw. (2011)

[22]

Zhang "Machine Learning Interface for Medical Image Analysis" J. Digit. Imaging (2017) 10.1007/s10278-016-9910-0

[23]

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Babak Alipanahi, Andrew Delong, Matthew T Weirauch et al.

Nature Biotechnology 2015 10.1038/nbt.3300

[24]

Identification of rice diseases using deep convolutional neural networks

Yang Lu, Shujuan Yi, Nianyin Zeng et al.

Neurocomputing 2017 10.1016/j.neucom.2017.06.023

[25]

Convolutional neural network for earthquake detection and location

Thibaut Perol, Michaël Gharbi, Marine Denolle

Science Advances 2017 10.1126/sciadv.1700578

[26]

Zhang "Automatic identification and classification in lithology based on deep learning in rock images" Acta Petrol. Sin. (2018)

[27]

Cheng "Study on Rock Image Classification Based on Convolution Neural Network" J. Xi’an Shiyou Univ. (Nat. Sci.) (2017)

[28]

Inc, G. (2018, August 05). TensorFlow. Available online: https://www.tensorflow.org/.

[29]

Ferreira "Convolutional Neural Network approaches to granite tiles classification" Expert Syst. Appl. (2017) 10.1016/j.eswa.2017.04.053

[30]

Boureau, Y.L., Ponce, J., and Lecun, Y. (2010, January 21–24). A Theoretical Analysis of Feature Pooling in Visual Recognition. Proceedings of the International Conference on Machine Learning, Haifa, Israel.

[31]

Blistan "Using UAV photogrammetry to document rock outcrops" Acta Montan. Slovaca (2016)

[32]

Vasuki "Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach" Comput. Geosci. (2014) 10.1016/j.cageo.2014.04.012

[33]

Zheng "UAVRS Technique Applied to Emergency Response Management of Geological Hazard at Mountainous Area" Appl. Mech. Mater. (2013)

[34]

(2018, August 05). Tzutalin LabelImg. Git code (2015). Available online: https://github.com/tzutalin/labelImg.

[35]

Zhang "A Return-Cost-based Binary Firefly Algorithm for Feature Selection" Inf. Sci. (2017)

[36]

Mask R-CNN

Kaiming He, Georgia Gkioxari, Piotr Dollar et al.

2017 IEEE International Conference on Computer Vis... 10.1109/iccv.2017.322

[37]

Wang "A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution" Appl. Soft Comput. (2018) 10.1016/j.asoc.2017.12.002

[38]

Zhang "Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification" IEEE/ACM Trans. Comput. Biol. Bioinform. (2017) 10.1109/tcbb.2015.2476796

[39]

Wang "An effective krill herd algorithm with migration operator in biogeography-based optimization" Appl. Math. Model. (2014) 10.1016/j.apm.2013.10.052

Cited By

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IEEE Access

Metrics

90

Citations

39

References

Details

Published: Aug 18, 2019
Vol/Issue: 7(8)
Pages: 755
License: View

Authors

X

Xiangjin Ran

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

L

Linfu Xue

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

Y

Yanyan Zhang

Jilin Business and Technology College, Changchun 130012, China

Z

Zeyu Liu

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

X

Xuejia Sang

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

J

Jinxin He

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

Funding

Department of Science and Technology of Jilin Province Award: 20170201001SF

Education Department of Jilin Province Award: JJKH20180161KJ

China Geological Survey Award: 1212011220247

Cite This Article

Xiangjin Ran, Linfu Xue, Yanyan Zhang, et al. (2019). Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network. Mathematics, 7(8), 755. https://doi.org/10.3390/math7080755

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

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