Abstract: We used augmented solar images to research the adaptability of four representative image extraction and matching algorithms in space weather domain, including the scale-invariant feature transform algorithm, speeded-up robust features algorithm, binary robust invariant scalable keypoints algorithm, and oriented fast and rotated brief algorithm. We estimated the performance of these algorithms in terms of matching accuracy, feature point richness and running time. We concluded that no algorithm achieved high accuracy while keeping low running time, and all algorithms are not suitable for image feature extraction and matching of augmented solar images. To solve this problem, we proposed an improved method by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm. Furthermore, we applied our method and the four representative algorithms to augmented solar images. The results of our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which significantly higher than other algorithms, and obtained similar low running time to the oriented fast and rotated brief algorithm which significantly lower than other algorithms.