# A Unified Framework of Bundle Adjustment and Feature Matching for High-Resolution Satellite Images

arXiv: https://arxiv.org/abs/2107.00598 · HTML: https://mlsanity.com/paper/2107.00598/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [Deep Learning Meets Satellite Images -- An Evaluation on Handcrafted and Learning-based Features for Multi-date Satellite Stereo Images](https://mlsanity.com/paper/2409.02825/) (2024)
- [Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences](https://mlsanity.com/paper/2607.26973/) (2026)

## Abstract (arXiv)

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.