Image segmentation is an important part of the standard digital rock physics (DRP) workflows. In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation which is validated using the extracted ground-truth. To generate the ground-truth, we utilize the a priori knowledge of minerals and pores. We perform texture based iterative intensity thresholding to calculate the porosity and mineral composition. The ground-truth is generated for the berea sandstone image dataset and the grosmont carbonate image dataset. Results obtained from the experiments conform to the porosity and composition ranges provided in the literature. For the purpose of automated segmentation, we use a SegNet deep network which performs pixel-wise segmentation of digital rock images into pores and minerals. We train and test the SegNet using 1024 images of berea sandstone dataset ground-truthed using iterative intensity thresholding. We utilize a split of 90% and 10% for training and testing, respectively. Experimental outcomes show that the SegNet-based segmentation results for the minerals such as clay, ankerite, and zircon are better than the existing methods.