With the development of drone-based aerial photography technology, unmanned aerial vehicle (UAV) images have become the main tools to create three-dimensional maps and monitor pit slopes for mining areas. To address the problem of poor feature-matching of high-resolution UAV images on pit slopes, we propose a new feature-matching algorithm that makes improvements in three main aspects: (1) A hybrid scale space is established in combination with a spatial domain scale and a frequency domain. The space of the spatial domain scale is generated with anisotropic diffusion filtering, and the space of the frequency domain scale is generated with a phase consistency filter and a weighted least square filter; (2) to distribute feature points uniformly across an image, we first partition images in the stage of feature point extraction and then extract feature points from each partition; (3) a boosted efficient binary local image descriptor is constructed for the detected interest points, thus enhancing the robustness of the descriptor and improving the matching speed. Eight groups were selected from the same UAV sequence images for feature-matching experiments, and the results were compared with classical algorithms such as scale-invariant feature transform, oriented features from accelerated segment test, rotated binary robust independent elementary features, and KAZE. It is found that our improved algorithm appropriately improves the matching speed under the condition of high matching accuracy and better matches the UAV images of pit slopes than the existing image registration methods.