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Objective：3D scene reconstruction based on multi-view satellite remote sensing images is a challenging task in the field of remote sensing. Most of the existing methods either have to perform bundle adjustment repeatedly or need to calculate a lot of parameters in the rational polynomial camera model, resulting in a relatively long reconstruction time. To solve the above-mentioned problems, this paper considers that the local small-sized patches in large-sized satellites could be approximately modeled by the affine imaging model, and proposes a fast 3D reconstruction method of satellite images based on global affine model estimation. Method: First, the input multi-view satellite images are cropped into a set of small-sized patches with overlapping regions. For each pair of patches that have a sufficient number of point correspondences from two views, the corresponding 3D affine point cloud is calculated. Second, based on the obtained local point clouds, a global affine camera motion estimation algorithm is presented for calculating the affine motion matrices of the cameras corresponding to all the patches in a unified coordinate system. Finally, the obtained affine camera motion matrices and a small number of ground control points are utilized to recover the Euclidean scene structure. Result: 3D reconstruction is carried out for the same group of remote sensing images and all remote sensing images to verify the effectiveness of the method The proposed solution is compared with three state-of-the-art methods (i.e., COLMAP, S2P, and JHUAPL). The experimental results on two public datasets (i.e., MVS3DM and DFC2019) show that the proposed method outperforms the three comparison algorithms in most cases with respect to speed, accuracy, and completeness. In order to further verify the reconstruction accuracy of the method, this paper selects 15 complex scene areas from two public datasets, including complex scenes with built-up areas, shadow areas, and complex object areas. For 15 complex scenarios, the proposed method outperforms the three methods with respect to accuracy and completeness in most cases. Conclusion: This paper proposes a fast reconstruction method of satellite images based on global affine model estimation algorithm. The method assumes that the local image tile in large-scale satellite remote sensing images conforms to the affine imaging model, and introduces a global affine motion matrix estimation algorithm based on local point clouds. As a result, the proposed solution can calculate the global affine motion matrix of each local image tile through only one bundle adjustment, significantly reducing the reconstruction running time. The experimental results show that the proposed method can quickly solve the global affine matrix corresponding to each image tile, and realize fast 3D reconstruction of remote sensing images.