Grain segmentation of sandstone that is partitioning the grain from its surrounding matrix/cement in the thin section is the primary step for computer-aided mineral identification and sandstone classification. The photomicrograph of sandstone contain many mineral grains and their surrounding matrix/cement. The distinction between adjacent grains and the matrix is often ambiguous, making grain segmentation difficult. Various solutions exist in literature to handle these problems; however, they are not robust against sandstone petrography’s varied pattern. In this paper, we formulate grain segmentation as a pixel-wise two-class (i.e., grain and background) semantic segmentation task. We develop a deep learning-based end-to-end trainable framework named Deep Semantic Grain Segmentation network (dsgsn), a data-driven method, and provide a generic solution. As per the authors’ knowledge, this is the first work where the deep neural network is explored to solve the grain segmentation problem. Extensive experiments on the images highlight that our method obtains better segmentation accuracy than various segmentation architectures with more parameters.

在薄片中将颗粒与其周围基质/水泥隔开的砂岩颗粒分割是计算机辅助矿物识别和砂岩分类的主要步骤。砂岩的显微照片包含许多矿物颗粒及其周围的基质/水泥。相邻晶粒和基体之间的区别通常不明确，使晶粒分割变得困难。文献中存在各种解决方案来处理这些问题；然而，它们对砂岩岩相的变化模式并不稳健。在本文中，我们将颗粒分割制定为像素级的两类（即颗粒和背景）语义分割任务。我们开发了一个基于深度学习的端到端可训练框架，名为 Deep Semantic Grain Segmentation network ( dsgsn)，一种数据驱动的方法，并提供通用的解决方案。根据作者的知识，这是探索深度神经网络来解决颗粒分割问题的第一项工作。对图像的大量实验表明，我们的方法比具有更多参数的各种分割架构获得了更好的分割精度。