Novel Methods for Separation of Gangue from Limestone and Coal using Multispectral and Joint Color-Texture Features

Abstract

Ore sorting is a useful tool to remove gangue material from the ore and increase the quality of the ore. The vast developments in the area of artificial intelligence allow fast processing of full-color digital images for the preferred investigations. The associated gangue minerals from limestone and coal mines were identified using three different approaches. All the methods were based on extensions of the co-occurrence matrix method. In the first method, the color features were extracted from RGB color planes and texture features were extracted using a multispectral extension, in which co-occurrence matrices were computed both between and within the color bands. The second method used joint color-texture features where color features were added to gray scale texture features. The last method used gray scale texture features computed on a quantized color image. Results showed that the accuracy for separation of gangue from limestone, a joint color-texture method was 98 % and for separation of gangue from coal, multispectral method with correlation and joint color-texture method were 100 % respectively. Combined multispectral and joint color-texture methods gave good accuracy with 64 gray levels quantization for separation of gangue from limestone and coal.