An improved method for nonnegative matrix decomposition and endmember extraction is proposed based on hyperspectral data simplification. Further, the homogeneous regions of images can be identified by calculating and comparing the spectral information entropy of various regions. Only the most representative pixels in the homogeneous regions are selected for application in the subsequent nonnegative matrix decomposition algorithm, which considerably reduces the amount of computation required in the endmember extraction algorithm. The experimental results show that although the mean values of the spectral angles of several kinds of minerals extracted using the nonnegative matrix factorization algorithm before and after data simplification are equal, the operation time of endmember extraction after data simplification is reduced by approximately 4/5, and the operating efficiency of the algorithm is improved.