Three-dimensional (3D) reconstruction based on aerial images has broad prospects, and feature matching is an important step of it. However, for high-resolution aerial images, there are usually problems such as long time, mismatching and sparse feature pairs using traditional algorithms. Therefore, an algorithm is proposed to realize fast, accurate and dense feature matching. The algorithm consists of four steps. Firstly, we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution. Secondly, to realize further screening of the mismatches, a feature screening algorithm based on similarity judgment or local optimization is proposed. Thirdly, to make the algorithm more widely applicable, we combine the results of different algorithms to get dense results. Finally, all matching feature pairs in the low-resolution images are restored to the original images. Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time, screen out the mismatches, and improve the number of matches.