Determination of basalt zones using basalt extraction index (BEI) and ASTER image classification

Abstract

Abstract One of the most important applications of remote sensing is presented in mining and exploration of mineral deposits and evaluation of prospective targets. This project discusses how to use remote sensing knowledge in order to make classification and separation of surface rocks in the Dir-o-Morreh mine. The main purpose of this research is to identify the areas containing high-quality basalt. In this regard, we utilize ASTER multi-spectral satellite imagery, which has relatively good spectral and spatial resolution. At the first step, in order to achieve the correct spectral composition of the basalt spectrum, the spectral signature of basalt stone, defined by Johns Hopkins University, was used. Afterward, the basalt extraction index (BEI) was defined regarding the behavior of the ASTER satellite image bands as well as the initial data provided by the owners of the intended study area. Then, the Convolution and Morphology filter was applied over the images to extract high-quality basalt using an appropriate color composition of the images. At the next step, in order to have better visualization, different maps containing different classes were created using the Maximum Likelihood algorithm. Finally, two indices were developed regarding all research data and field investigations in order to extract basalt zones. The first index discovers basalt zones in the study area, and the second one classifies high-quality basalt and altered basalt zones.