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

Silvia, Joseph and Hamimah, Ujir and Irwandi, Hipiny (2017) Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 12-14 September 2017, Kuching.

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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.

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