An Epipolar Resampling Method for Multi-View High Resolution Satellite Images Based on Block

Abstract

As the basis of stereo observation, epipolar resampling is a critical technology for 3D reconstruction. With the development of the 3D reconstruction using multi-view satellite images, the epipolar resampling of the multi-view satellite images has become a new challenging problem. In this paper, we propose an epipolar resampling method for multi-view high resolution satellite images based on block. Firstly, we establish the relationship between the vertical parallax and image block size for the epipolar resampling based on block. Using this relationship, we can choose the block size to limit the maximum vertical parallax in epipolar resampling. Secondly, we use the rational function model (RFM) to generate virtual corresponding points for fundamental matrix estimation, so image pairs with large stereo angles and poor consistency in multi-view satellite images which are difficult to extract feature matching point can be rectified. Experiments with multi-view satellite images demonstrated the universality and effectiveness of the proposed method. All the image pairs in the multi-view satellite image dataset were rectified successfully. The average epipolar resampling error is only 0.25 pixels when we rectify the multi-view satellite images acquired by WorldView-3 with the block size of 3000*3000 pixels, significantly less than the similar method.INDEX TERMS Epipolar resampling; multi-view; push-broom satellite image; block size; fundamental matrix.

Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

“…By generating epipolar images through epipolar constraints, stereo matching is transformed from a complex 2D search process to a 1D one, simplifying the stereo matching algorithm while improving its running speed and reliability. Figure 1 shows the epipolar constraint results for the generalized stereo image pairs (with dataset 1 and dataset 2 as examples), from which we can see that the search range of matching points is only in the x-direction of the image, which greatly reduces the complexity of the algorithm [43].…”