  This paper proposes a strategy for efficient geometrical verification in
unmanned aerial vehicle (UAV) image matching. First, considering the complex
transformation model between correspondence set in the image-space, feature
points of initial candidate matches are projected onto an elevation plane in
the object-space, with assistant of UAV flight control data and camera mounting
angles. Spatial relationships are simplified as a 2D-translation in which a
motion establishes the relation of two correspondence points. Second, a
hierarchical motion consistency constraint, termed HMCC, is designed to
eliminate outliers from initial candidate matches, which includes three major
steps, namely the global direction consistency constraint, the local
direction-change consistency constraint and the global length consistency
constraint. To cope with scenarios with high outlier ratios, the HMCC is
achieved by using a voting scheme. Finally, an efficient geometrical
verification strategy is proposed by using the HMCC as a pre-processing step to
increase inlier ratios before the consequent application of the basic RANSAC
algorithm. The performance of the proposed strategy is verified through
comprehensive comparison and analysis by using real UAV datasets captured with
different photogrammetric systems. Experimental results demonstrate that the
generated motions have noticeable separation ability, and the HMCC-RANSAC
algorithm can efficiently eliminate outliers based on the motion consistency
constraint, with a speedup ratio reaching to 6 for oblique UAV images. Even
though the completeness sacrifice of approximately 7 percent of points is
observed from image orientation tests, competitive orientation accuracy is
achieved from all used datasets. For geometrical verification of both nadir and
oblique UAV images, the proposed method can be a more efficient solution.
