In hyperspectral imagery, endmember extraction is the process of finding a pure spectrum set within the materials present in a hyperspectral scene. However, various endmember extraction algorithms (EEAs) can yield different endmember spectrum sets. This research presents a comparison of four EEAs: pixel purity index, automatic target generation process (ATGP), N-finder (N-FINDR), and simplex identification via split augmented Lagrangian. To perform the comparison, a ground reference geological map is first coregistered with the hyperspectral scene. Then, 12 geological ground truth spectra are chosen. The four EEAs are then used to extract endmember spectra from the scene. Next, the extracted endmember spectra are applied to generate abundance maps using fully constrained least squares. The largest spectrum magnitude in the abundance map is considered the endmember. Finally, the spectral angle mapper and root-mean-square error between the extracted endmember spectrum and the chosen geological spectral spectra are computed, using the angle and minimum error to evaluate similarities. The results of this EEA comparison show that only the ATGP algorithm could consistently identify endmembers with the least computation time. Additionally, the N-FINDR algorithm is able to extract the most endmembers with the closest endmember similarity measures, although this required the highest computation time.