Open AccessA Unified Framework of Bundle Adjustment and Feature Matching for High-Resolution Satellite Images
Author(s) -
Xiao Ling,
Xu Huang,
Rongjun Qin
Publication year - 2021
Publication title -
photogrammetric engineering and remote sensing
Language(s) - English
Resource type - Journals
eISSN - 2374-8079
pISSN - 0099-1112
DOI - 10.14358/pers.87.7.485
Subject(s) - bundle adjustment , matching (statistics) , degeneracy (biology) , feature (linguistics) , satellite , artificial intelligence , computer science , bundle , global optimization , orientation (vector space) , feature matching , function (biology) , pattern recognition (psychology) , algorithm , computer vision , mathematics , feature extraction , image (mathematics) , geometry , engineering , statistics , bioinformatics , linguistics , philosophy , materials science , composite material , evolutionary biology , biology , aerospace engineering
Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results. Feature matching often contains high uncertainties in weak/repeat textures, while BA results are helpful in reducing these uncertainties. To compute more accurate orientations, this article incorporates 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.