The unmanned aerial vehicle (UAV) multi-view matching method in image space ignores the geometric relationship among images. The multi-view matching method in object space represented by modified vertical line locus (MVLL) does not consider the mutual restraint of terrains. A multi-view matching algorithm that is based on the advantages of two multi-view matching methods is proposed in this study. An UAV multi-view matching algorithm in object space is presented by adding a semi-global matching compatibility constraint to the MVLL structure. This algorithm not only inherits the semi-global matching advantages of exhibiting good matching performance on weak texture areas and object boundaries but also prevents the tedious production of original semi-global matching algorithm-required rectified images. A summed normalized cross-correlation (SNCC) consensus cost function of object window is presented to reduce the effects of camera angles and occluded areas. The matching speed and reliability of the algorithm are improved by pyramiding strategies. In the experimental part

high-resolution images

which are obtained from an independently developed rotor UAV three-axis stabilized platform

are used to effectively test the matching results. The algorithm is tested from three aspects

namely

the matching effect

performance of the new matching measure

and matching precision. The experimental results show that this algorithm displays good matching effect. The matching measure of the SNCC of the object square window can effectively eliminate the gross error in the matching measure. The point cloud data generated by the proposed matching method in the elevation direction can reach 0.004 9 m which is equivalent to the size of 1 GSD(ground space resolution). The method utilizes the multi-view information of a UAV image for matching calculation

and it demonstrates good matching effect

strong robustness

and high matching precision.