Abstract
Conventional convolutional neural networks (CNN) are deficient in the rock type recognition due to large convolutional kernels and numerous network parameters necessitated for recognition of complex images. The advanced convolutional neural network, Visual Geometry Group-16 (VGG16) model, which is based on multiple small convolutional kernels and fully connected layers, attains higher classification accuracy, yet is limited by low computational efficiency. In this paper, we propose a novel approach that integrates the advanced VGG16 with the Principal Component Analysis (PCA), a dimensionality reduction technique. This integration, referred to as the PCA-VGG16 model, aims to enhance the computational efficiency of automatic rock type identification. A dataset comprising 3000 images of six rock types: limestone, shale, dolomite, quartzite, marble, and granite, is assembled for training and testing of the PCA-VGG16 model. The feasibility of the PCA-VGG16 model for classification prediction is demonstrated through evaluation metrics including accuracy, loss value, and F1-score. A comparative analysis with the CNN and VGG16 models reveals that the proposed PCA-VGG16 model exhibits superior classification accuracy and reduced training durations, making a very promising advancement in the field. Furthermore, an in-depth analysis is conducted to understand the impact of dataset size and key hyperparameters (such as epochs and batch size) on the classification accuracy of the PCA-VGG16 model. The findings indicate that a minimum dataset of 1500 sample images is necessary to achieve a classification accuracy above 90%. For optimal model performance, a division of training, validation, and test sets approximately at 6:2:2, along with two epochs and a batch size of 128, is recommended in this study.
