针对无人机航拍影像的高分辨率特点, 用简化的尺度不变特征变换(SIFT)算法提取待匹配图像中的特征点并粗匹配。根据航拍图像的特殊获取方式, 提出一种基于航向约束的特征点提纯算法, 并用实验进行验证。结果表明, 此算法能有效提纯匹配点, 提纯率达到25%, 与随机抽样一致(RANSAC)算法比较, 在保持提纯率的前提下, 效率提高了将近一倍。

According to the high resolution of UAV aerial images, the simplified Scale Invariant Feature Transform (SIFT) algorithm is used to extract the feature points and complete coarse matching.Considering the special access of aerial images, a new method for feature point purification is proposed based on course constraint.Experimental results show that this method can purify the matching points effectively, and the purification rate runs up to 25%.Compared with Random Sample Consensus (RANSAC) algorithm, the efficiency is nearly doubled while maintaining the purification rate.