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

• URL: http://arxiv.org/abs/2107.00598v1
• Date: Thu, 1 Jul 2021 16:40:25 GMT
• Title: A Unified Framework of Bundle Adjustment and Feature Matching for High-Resolution Satellite Images
• Authors: Xiao Ling, Xu Huang, Rongjun Qin
• Abstract summary: This article incorpo-rates Bundle adjustment (BA) and feature matching in a unified framework. Experiments on multi-view high-resolution satellite images show that our proposed method outperforms state-of-the-art orientation techniques.
• Score: 4.835738511987696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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