A Unified Framework of Bundle Adjustment and Feature Matching for High-Resolution Satellite Images
This work addresses the challenge of accurate 3D reconstruction from satellite imagery, which is incremental as it builds on existing bundle adjustment and feature matching methods.
The paper tackles the problem of improving sensor orientation accuracy for high-resolution satellite images by jointly optimizing bundle adjustment and feature matching within a unified framework, resulting in a method that outperforms state-of-the-art orientation techniques in experiments.
Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results. Feature match-ing often contains high uncertainties in weak/repeat textures, while BA results are helpful in reducing these uncertainties. To compute more accurate orientations, this article incorpo-rates BA and feature matching in a unified framework and formulates the union as the optimization of a global energy function so that the solutions of the BA and feature matching are constrained with each other. To avoid a degeneracy in the optimization, we propose a comprised solution by breaking the optimization of the global energy function into two-step suboptimizations and compute the local minimums of each suboptimization in an incremental manner. Experiments on multi-view high-resolution satellite images show that our proposed method outperforms state-of-the-art orientation techniques with or without accurate least-squares matching.