The experimental results show that the SIFT algorithm yielded the best performance. However, it was apt to be affected by image noise and texture changes and was computationally intensive and time-consuming during feature point extraction [26]. To solve these problems, several improved versions of the SIFT algorithm were proposed. For instance, the independent component analysis-SIFT (ICA-SIFT) algorithm, which uses independent component analysis to remove redundant information in the SIFT feature vectors, was proposed [27]. It improved the efficiency but decreased the accuracy. The affine-SIFT (ASIFT) algorithm proposed by [PERSON] and [PERSON] [28] achieved complete affine invariance by simulating longitude and latitude, but its efficiency was much lower than that of the SIFT algorithm. The uniform robust SIFT (UR-SIFT) algorithm [29] is suitable for multisource optical RS images with different illumination conditions, rotations, and five-fold scales. However, the crossmatching approach employed by the UR-SIFT algorithm eliminateda number of correctly matched tie points, which reduced the accuracy. Inspired by the SIFT algorithm, an enhanced feature matching method by combining the position, scale, and orientation of each keypoint, which is called PSO-SIFT, was proposed [30]. The PSO-SIFT method effectively increased the number of correctly matched tie points, but it also increased the running time. However, for images with complex terrain and surface deformations caused by disasters, these algorithms can hardly meet the requirements of accuracy and efficiency simultaneously. Through the improvement of the corner response function of Harris, the Shi_Tomasi corner detection algorithm is not susceptible to image rotations, lighting conditions, angle changes, and noise. Additionally, it avoids the phenomenon of feature point clustering, which makes the distribution of extracted feature points more even [31]. Additionally, the Shi_Tomasi algorithm needed less running time than the SIFT algorithm and thus effectively improved the efficiency of feature extraction. Due to surface deformations caused by severe earthquakes, post-earthquake RS images usually show surface damage such as building collapse, ground displacements, road damage, and landslides, making it more challenging for the coregistration of pre- and post-earthquake RS images. Many algorithms work well for RS image registration, but when used for the registration of pre-earthquake and post-earthquake RS images, especially for mountain areas with complex terrain, two problems arise, including the lack of tie points and the uneven distribution of matched tie points. To solve these two problems, a fast automatic registration method for multisource high-resolution RS image registration in earthquake damage assessment was proposed in this study. In this method, the Shi_Tomasi and SIFT algorithms were combined to obtain tie points using an image partition matching strategy considering geographic information. The proposed method was compared with the classic SIFT method and the SIFT method using the same image partitioning strategy using three pairs of post- and pre-earthquake images that have complex terrain and significant differences in color tone. The rest of this paper is organized as follows. The proposed method is introduced in Section 2, and the experiments are demonstrated in Section 3. The discussions are provided in Section 4, and the conclusions are presented in Section 5.