With over four decades spent collecting spaceborne moderate-resolution imagery, Landsat represents the longest remote sensing mission in the world, and has had various applications. Land cover mapping is its heritage for research around the world. Landsat 8 continues the legacy of previous Landsat systems, with a new Operational Land Imager (OLI) sensor that has high spectral resolution and improved signal-to-noise ratio for better characterization of land cover. With improved quality, data size also increases. Hence, with limited research in adjusting data size, it is necessary to explore robust land cover classification techniques that produce accurate maps with more or fewer inputs. The Optimum Index Factor (OIF) is a statistic value that can be used to select the optimum combination of three bands in a satellite image that has the highest amount of information. In this study, we explore the land cover classification of OLI imagery based on OIF. Two test sites were selected around the hilly regions of Korea for OLI original composite, first-rank OIF composite, and OLI original with sum derivative of top-three OIF ranked composites. These three composites were classified with the well-known Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classifiers. The results were then analyzed and compared on the basis of producer accuracy, user accuracy, overall accuracy, and kappa coefficient. The result shows that the first-ranked OIF with a three-band composite shows a similar classification accuracy in SVM and slightly less in SAM, while the ten-band composite with OLI original bands and the sum derivative of the top-three OIF rank shows the same result or a small improvement in SVM classification. OIF-derivative composites can be useful in classification problems depending on whether the minimum amount of data for a similar result or more data to achieve higher accuracy is preferred.