| dc.description.abstract | Carbonate outcrops are one of the focuses of research in the oil industry. In situ research is the most efficient way to characterize an outcrop, though it requires time and high investments. Analysis using remote sensing data converted into indicators that attest to the presence of certain materials on the study site, such as spectral indexes or fraction images, represents a viable alternative. Although spectral indices for carbonate rocks already exist, they present limitations mostly related to the input data’s resolution and the indicator’s extension to other environments. The present master’s thesis proposed approaching this problem in a structured way to formulate a new index that surpasses those existing in the literature’s performance. The present master’s thesis proposed approaching this problem in a structured way to formulate a new index that surpasses those existing in the literature’s performance. The mining procedure was used on the available data to understand how decisions are made in order to separate carbonate targets from any other material. Afterward, data scatter plots were produced to locate the region that concentrates targets of interest in the multispectral space, as well as to visualize how they differ from other targets. Finally, a versatile spectral index for carbonate rocks was formulated, with parameters adjusted according to the sensor in use (in this case, the OLI-Landsat 8 sensor), offering the user the possibility to adapt the index to his scenario. The index was calculated for an image and compared with a Ground Truth (GT) produced from visual interpretation followed by data degradation to reach the appropriate scale in each situation. The proposed index followed all the premises indicated by the literature and achieved a performance of 83.2 % of global accuracy, a value relatively higher than the other indexes referenced in the current literature. These results suggest the potential for further research to test the index application to other study areas and other sensors’ data (with the necessary adjustments). | en |
