基于改进SURF算法的无人机遥感图像配准

UAV Remote Sensing Image Registration Based on Improved SURF Algorithm

对无人机视觉导航图像配准SURF(speeded up robust features)算法进行改进,将SURF特征提取与BRISK特征描述相结合,提出SURF-BRISK算法。首先,采用相对运算速度更快的FLANN(fast library for approximate nearest neighbors)算法粗匹配特征点。然后,对错误匹配的特征点使用鲁棒性较好的RANSAC(random sample consensus)算法进行筛选与剔除。最后,与SIFT(scale-invariant feature transform)算法及SURF算法进行对比。结果显示:在城市场景SURF-BRISK算法的运行时间相对于SIFT、SURF算法分别减少了约86%、74%;配准精度分别提高了约7%、13%;匹配点的对数也大幅提升。

The UAV visual navigation image registration algorithm,SURF(speeded up robust features)algorithm,is improved and SURF-BRISK algorithm was proposed:the SURF feature extraction was combined with BRISK feature description,the FLANN(fast library for approximate nearby neighbors)algorithm with relatively faster operation speed was used to roughly match feature points,and the RANSAC(random sample symptoms)algorithm with better robustness was used to filter and eliminate the mismatched feature points.The comparison experiment with SIFT(scale invariant feature transform)algorithm and SURF algorithm was carried out.Taking the urban scene as an example,the running time of SURF-BRISK algorithm was reduced by 86%and 74%respectively compared with SIFT algorithm and SURF algorithm,while the registration accuracy was improved by 7%and 13%respectively,and the logarithm of matching points was also greatly improved.