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Image classification technology has made tremendous progress with the development from shallow network (neural network) to deep neural network, image classification based on deep neural network has become popular in the field of image classification technology. By introducing shallow network and deep neural network, and making classification for 30 rock thin section images and contrast according to connectivity of pores with examples of the BP neural network (hidden layer has 6 neuron nodes and 7 neuron nodes) from shallow network and the convolutional neural network from deep neural network, finally, the average error rate of classification for deep neural network is 0%, and the average error rate of classification for BP neural network are 24.666% and 19.334% respectively, which shows that rock thin section image classification based on deep neural network has higher efficiency and better classification result than rock thin section image classification based on shallow network.

Rock Thin Section Image Classification Research from Shallow Network to Deep Neural Network