Remote-Sensing Image Stitching Based on Infinite Homography and Global Seam Finding

Abstract

We propose a stitching method of multiple remote-sensing images based on infinite homography and global seam finding to reduce edge stretching and seam errors and improve the spatiotemporal efficiency. First, by assuming that the scene is far away from the camera, we construct and use an infinite homography instead of mesh-based warping to improve the speed and robustness of stitching. Due to the fact that the aircraft always flies horizontally, we add the horizontal flight prior into the camera parameter optimization to solve the edge stretching problem. Second, we globally find the optimal seam between multiple images and convert the global seam finding into a multi-label assignment to obtain a smaller seam error. In the iteration of alpha expansion, by dynamically adjusting the weight of the pixel to the terminal, the pixels are induced to be assigned to the label with more allocated pixels, which reduces the number of input images participating in image blending. Finally, comparative experiments with other methods prove that our method has less alignment error and edge stretching. Furthermore, our method is more robust, occupies less memory and runs faster, which makes our method more effective and practical in multiple image stitching.