This paper investigates the 3D reconstruction of massive remote sensing images. We propose a novel reconstruction framework (MPB) that explicitly decomposes the reconstruction task into three parts, which include image mosaic, camera pose estimation and bundle adjustment. Image mosaic includes feature detection, image matching, image registration. All remote sensing images will be registrated into a plane to establish the plane model of terrain. Then we use EPnP algorithms by RANSAC filter to estimate the camera pose, and use iterative method to get best camera pose. Bundle adjustment the camera poses and the terrain coordinates to complete the terrain 3D reconstruction. The above ideas are correct and feasible, which are measured by massive data of different terrain. The results show that this method can greatly improve the reconstruction efficiency, which provides a fast and efficient approach for 3D reconstruction of massive remote sensing images.
