Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks

Grain segmentation of sandstone images is to segment the mineral grains into separate regions, which is the first step for mineral identification and sandstone classification. A sandstone image contains a large number of mineral grains, which due to the complexity of the micro-structures and the variability of the optical properties, becomes difficult for automatic segmentation. In this paper, we propose a three-stage method for grain segmentation taking multi-angle sandstone images as the input. In the first stage, the pixels of the images are clustered by both color and spatial properties into superpixels. In the second stage, convolutional neural network (CNN) is trained based on replicated images of sandstone mineral grains, which is then used to extract the convolutional features of the superpixels. In the third stage, the fuzzy clustering algorithm is used to merge the over-segmented superpixels into mineral grains. We collect sandstone images from Tibet, based on which the experimental results demonstrate the following: (1) The convolutional features extracted by the designed CNN is more suitable for characterizing the mineral grains of sandstone images, comparing to the handcrafted features. (2) The proposed three-stage method is more effective than the state-of-the-art segmentation methods.