Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction - Chenlu Guo; Zhiyuan Li (Advances in Multimedia)
Lithology identification of rocks is an important part in the field of oil and gas exploration, mineral exploration, and geological analysis. How to accomplish rock classification is a key issue for the further development of the geology industry. The current main method for classifying rock pictures containing background is to select sample points or disregard the disturbance of the background. For more accurate classification, the rock part extraction method for rock images containing boundaries is designed to eliminate the influence of background. First, the rock parts are extracted based on the image gradient information and color information, respectively. Then, the two images are intersected to realize the refinement of pixel-level information to obtain a pure rock image. Ensemble ResNet18 (ERN18) is designed as an image classification model. It contains basic blocks to reduce the loss of features during the training process. The method breaks the neglect of most previous studies on background interference. The effect of misclassification in certain regions on the results is eliminated by ensemble learning based on the voting method. The classification results are further improved. Compared with the effects of LeNet, AlexNet, and ResNet, ERN18 has achieved significant results.