Siamese Adversarial Network for image classification of heavy mineral grains

Abstract

The identification of heavy mineral grains based on microscopic images can significantly reduce the time and economic cost of the identification. There are several deep learning models to realize end-to-end identification of mineral image recently. However, due to the variety and complexity of mineral images, the existing models are difficult to accurately recognize heavy mineral grains in microscopic images. Here we propose the Siamese Adversarial Network (SAN) for image classification of the heavy mineral grains, which is the first time to focus on addressing the domain difference of heavy mineral images from different basins. In more details, we design a Siamese feature encoder to extract features of both the plane-polarized and cross-polarized images as internal representation of heavy mineral grains. The features are reconstructed to discard domain-related information by adversarial training the heavy mineral classifier and domain discriminator. The identification performance of the models under the three mixed domain experiments is consistently higher than the performance under the same domain settings respectively which shows that the model we proposed achieves a great generalization ability on unseen domains.