When the patch-based multi-view stereo (PMVS) algorithm is applied to the three-dimensional (3D) scene reconstruction of multi-view remote sensing terrain images, due to the existence of weak texture and areas with no obvious change of gray value, there is the phenomenon that the reconstructed 3D terrain point cloud has low density of overall distribution and local holes. Combining the characteristics of remote sensing terrain images, this paper proposes an improved PMVS algorithm based on the concurrent SIFT operator of the image block and the ground elevation scope constraint. Firstly, uniform feature points with dense distribution are obtained in the feature extraction stage. Then through the propagation process of matching based on the ground elevation scope constraint, the seed patches are efficiently calculated. Finally, the 3D point cloud data of the terrain images is obtained by the iteration of the expansion and by filtering of seed patches. Experimental results show that compared with the original PMVS algorithm, the improved PMVS algorithm in this paper can reconstruct dense point clouds on multi-view remote sensing terrain images with wide and weak textures, effectively repair holes in the terrain point cloud scene, and improve the reconstruction time efficiency.