Abstract
The issue of classifying rock images is one of most important problems in geological detection and analysis. In order to improve rock recognition performance, we use convolution neural network (CNN) based deep learning method to classify ihomogeneous rock images intelligently. We present the depthwise separable convolution method for rock image classification, which reduces the required parameters compared to normal convolution, and achieves the separation of channels and regions. In our experiments, there are 12 kinds of common rock image data collected by us. Generally, similar rocks such as limestone and dolomite are easy to be confused, which is the same as that of rock with naked eyes. Compared with the model Inception, the rock image classification accuracy can be improved 9% by using the depth separable convolution model Xception. Meanwhile, after analyzing feature maps of granite and slate generated by the proposed model, we can easily find that the color, mineral composition and structure of the rock image are extracted successfully.
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Acknowledgment
This work is supported by the National Key R&D Program of China under Grant 2016YFC0600510, and National undergraduate innovation and entrepreneurship training program of Chengdu University of Technology, and the Key Laboratory of Geological Information Technology of Ministry of Land and Resources under Grant.
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Zhu, Y., Bai, L., Peng, W., Zhang, X., Luo, X. (2019). Depthwise Separable Convolution Feature Learning for Ihomogeneous Rock Image Classification. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-7983-3_15
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DOI: https://doi.org/10.1007/978-981-13-7983-3_15
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