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

TL;DR

This paper presents a unified framework combining bundle adjustment and feature matching to improve the accuracy of sensor orientations in high-resolution satellite images, addressing uncertainties in feature matching.

Contribution

It introduces a novel joint optimization approach that constrains bundle adjustment and feature matching, with a two-step incremental solution to avoid degeneracy.

Findings

Outperforms state-of-the-art orientation techniques

Improves accuracy in weak/repeat textures

Effective on multi-view high-resolution satellite images

Abstract

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.