The KAZE algorithm typically extracts feature points of low accuracy and mismatches in remote sensing images.Thus, this paper proposed a preprocessing algorithm to accelerate KAZE feature extraction. The proposed algorithm preprocessed the remote sensing image based on entropy constrained and KAZE feature extraction. The method first used a non-overlapping sliding window to traverse the remote sensing image and segmented the window area, and the entropy of the segmented window area was sequentially calculated.According to the histogram formed by the obtained entropy, an appropriate threshold was then selected to retain the local area of the image with high entropy for the KAZE algorithm feature extraction.Finally, the RANSAC algorithm was used to remove mismatches to optimize matching results. Experiments on the SPOT, GH-2 satellite data indicate that compared with the KAZE algorithm alone, the accuracy of the KAZE algorithm coupled with the proposed algorithm is improved by 0.2%, 0.3%, and the performance time of the algorithm is reduced by 70%, 53%, respectively.