In order to solve the problems that maximum simplex volume-based endmember extraction algorithms (EEAs) involve processing entire pixels and are sensitive to noises, this paper proposes a robust maximum simplex volume-based EEA for quickly selecting endmembers from hyperspectral images. The proposed algorithm first applies principal component analysis to reduce the hyperspectral image into p-1 subspace. It then detects convex hull points from each component pair by employing a convex hull algorithm. Next, it iteratively specifies p points and their simplex volume until they can provide a maximum simplex volume. Finally, it transforms p points into original dimensionality and obtains p denoised endmembers. Experiments conducted on synthetic and real hyperspectral images demonstrate that the proposed algorithm can quickly extract endmembers from the denoised hyperspectral. The proposed method can perfectly meet the requirements of high endmember accuracy and real-time in the field of hyperspectral endmember extraction.