Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis

TL;DR

This paper presents an automated image analysis method for classifying intrusive igneous rock thin sections using edge detection and color analysis, achieving high accuracy without expert intervention.

Contribution

It introduces a novel automated approach combining edge detection and color histogram analysis for rock classification from microscopic images.

Findings

Achieved classification precision between 90% and 100%.

Demonstrated effectiveness of grid-based cell analysis for rock identification.

Validated method with 60 images from 20 thin sections.

Abstract

Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision.