The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-2/W1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-2/W1-2021, 113–118, 2021
https://doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-113-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-2/W1-2021, 113–118, 2021
https://doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-113-2021

  15 Apr 2021

15 Apr 2021

AUTOMATIC IDENTIFICATION OF MINERALS IN IMAGES OF POLISHED SECTIONS

A. V. Khvostikov1, D. M. Korshunov2, A. S. Krylov1, and M. A. Boguslavskiy2 A. V. Khvostikov et al.
  • 1Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
  • 2Faculty of Geology, Lomonosov Moscow State University, Moscow, Russia

Keywords: Image Segmentation, Deep Learning, Geology, Mineral Identification, Polished Sections, Ore

Abstract. Automatic identification of minerals in images of polished section is highly demanded in exploratory geology as it can provide a significant reduction in time spent in the study of ores and eliminate the factor of misdiagnosis of minerals. The development of algorithms for automatic analysis of images of polished sections makes it possible to create of a universal tool for comparing ores from different deposits, which is also much in demand. The main contribution of this paper can be summed up in three parts: i) creation of LumenStone dataset (https://imaging.cs.msu.ru/en/research/geology/lumenstone) which unites high-quality geological images of different mineral associations and provides pixel-level semantic segmentation masks, ii) development of CNN-based neural network for automatic identification of minerals in images of polished sections, iii) implementation of software tool with graphical user interface that can be used by expert geologists to perform an automatic analysis of polished sections images.