遥感图像匹配是图像校正、拼接的基础。由于遥感图像特征相似度大, 重叠区域小, 遥感图像对匹配算法的要求更高。本文首先从特征检测、特征描述和特征匹配三个方面, 比较了SIFT算法和SURF算法在计算速度和准确度方面性能, 然后研究了算法对遥感图像重叠度、度量距离的要求, 并针对SURF算法对特征方向误差敏感的特点, 提出一种oSURF算法; 最后利用卫星1A级条带遥感图像分析各个算法优劣性。测试结果表明, 相比于SIFT算法, SURF算法计算速度为SIFT的3倍, 需要的图像重叠宽度仅为1.25倍描述向量尺寸, 而在保证同样匹配率的情况下, SIFT算法则需要图像重叠宽度为1.5倍描述向量尺寸。本文提出的 oSURF算法在保证计算速度的同时, 准确度相对于SURF算法提升5%~10%, 因此, oSURF算法更适合1A级条带遥感图像的拼接。

Image matching is the basis of image rectification and mosaic. Because of higher features similarity and smaller overlapped area than ordinary images, the remote sensing images have higher requirements on matching algorithm in both performance and iteration speed. The performances in three aspects: feature detection, feature description and feature matching, are analyzed between the SIFT algorithm and the SURF algorithm in terms of speed and accuracy. The requirements of the degree of overlapping between remote sensing images and the matching distance of the genvector is discussed as well. In view of the characteristic that SURF algorithm is sensitive to the error in feature detection, oSURF algorithm is presented in this paper. Finally, the advantages and disadvantages of each algorithm are analyzed by using satellite remote sensing data of level 1A. The results show that iteration speed of SURF algorithm is three times faster than SIFT algorithm. Under the same matching rate, the width of overlapped area on image required in SURF algorithm is 1.25 times of the dimension of genvector but 1.5 times in SIFT, and the accuracy of oSURF algorithm is increased by 5%~10% compared with SURF algorithm in the same computation speed, which indicate that oSURF is more suitable for remote sensing image stitching.