Abstract: Objectives: Incremental structure from motion (ISfM) has become the widely used workflow for aerial triangulation (AT) of unmanned aerial vehicle (UAV) images. However, it suffers from significant issues of error accumulation and efficiency degradation. The existing parallelization methods for ISfM suffer from issues such as the scene graph construction ignoring the local connection strength of images, long merging paths for sub-scenes (spatially related image collections), and low efficiency, making it difficult to meet the requirements of large-scale scene image processing. This paper proposes a parallelized SfM method for UAV images that does not rely on fixed anchor points (reference models). Methods: First, the proposed solution designs a scene graph construction algorithm that considers local connection strength constraints, effectively improving the compactness of scene graph partitioning and ensuring the high robustness of SfM reconstruction within partitions. Second, it proposes a parallelized sub-scene merging method that does not rely on fixed anchor points, enabling adaptive adjustment of merging paths and parallel merging of multiple sub-scenes, significantly enhancing the reliability and efficiency of sub-scene merging. Third, to avoid the influence of outliers in corresponding geometric elements, this paper employs the RANSAC hypothesis-verification framework for robust parameter estimation and designs a transformation model estimation based on a bidirectional mean square reprojection error metric. Results: Experiments using real UAV images demonstrate that the proposed method achieves a 9 times speedup in sub-scene merging, with relative and absolute orientation performance comparable to existing methods. At the same time, the proposed method has also been successfully applied to the parallel SfM reconstruction of nearly 90,000 UAV images that covers an aera of 50 km². Conclusions: Considering the metric of efficiency and precision, the proposed algorithm can be an validate solution for the efficient SfM reconstruction for large-scale UAV datasets.