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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM
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UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM

机译:基于改进的RANSAC算法和冲浪算法的无人空中车辆图像匹配

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摘要

A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.
机译:提出了一种基于RANSAC(随机样本共识)算法和冲浪(加速鲁棒特征)算法的UAV图像匹配方法。冲浪算法与快速操作和良好的旋转不变性,缩放不变性和照明集成。亮度是不变的,鲁棒性很好。 RANSAC算法可以有效地消除不匹配点对的特征。验证实验和基本验证实验被添加到RANSAC算法中,从而提高了算法的拒绝和运行速度。实验结果表明,与冲浪算法相比,SIFT(尺度不变特征变换)算法和ORB(定向快速和旋转简短)算法,所提出的算法在匹配精度和匹配速度方面优于其他算法,以及鲁棒性更高。

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