Identification of plagioclase extinction-angle features from polarized images using deep neural network.

Abstract

Plagioclase is a principal component of the Earth's crust, whose compositional and structural analysis is vital for understanding the crust's construction and evolution. Accurate identification of extinction angle features plays an important role in determining the sodium-calcium content in plagioclase. Manual evaluation of these extinction angle features is tedious and dependent on human expertise. Additionally, current image recognition methods for identifying plagioclase extinction angles face challenges such as information loss, weak stripe features, and difficulty in capturing long-range spatiotemporal features. To address these challenges, we propose an extinction angle identification neural network called AFI-Net, which utilizes polarized image sequences for the accurate detection of plagioclase's extinction angle features. AFI-Net combines a 2D convolutional neural network with a Transformer. Initially, a stripe attention module is developed to enhance the network's ability to detect stripe features. Building upon this module, a 2D backbone network is designed to efficiently extract spatial features from polarized images. The spatial features are then fed into a customized Transformer-based module to extract spatiotemporal features. Ultimately, these spatiotemporal features are used to accurately identify the extinction angle features of plagioclase. Extensive quantitative and qualitative experimental results demonstrate that AFI-Net achieves high accuracy and stability in recognizing the extinction angle features of plagioclase, showing significant superiority over current advanced recognition methods.