To overcome the shortcomings such as poor adaptability, low efficiency, and the demand for prior knowledge in the 3D reconstruction using UAV images, this study proposed a method for extracting match pairs of UAV images considering geospatial information. The steps of this method are stated as follows. Firstly, reduce high-dimensional features of the images to low-dimensional features using the principal component analysis (PCA) method to improve the construction efficiency of the retrieval vocabulary. Secondly, construct a comprehensive retrieval factor by calculating the inverse distance weighting factor between query images to improve the distinguishability between similar images. Finally, discard invalid match pairs by calculating the retrieval threshold to improve the query precision of images. The experimental results show that, compared to the traditional footprint map method and 128-dimensional feature retrieval method, this method enjoys higher processing efficiency and more comprehensive sparse reconstruction results, especially for the massive UAV data.
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