SURF（Speed Up Robust Features）算法是对尺度不变特征变换SIFT（Scale Invariant Feature Transform）算法的一种改进，应用到遥感图像匹配领域中可以大大提高匹配速度，但是匹配精度略有下降。为此，本文提出一种基于无下采样Contourlet变换NSCT（Nonsubsampled Contourlet Transform）和SURF的遥感图像匹配算法。首先使用NSCT分别分解参考图像和待匹配图像，得到各自对应的低频分量；然后把这两幅低频分量图像作为SURF算法的输入图像进行预匹配，降低高频噪声对匹配结果的影响；最后利用预匹配结果求解变换模型的参数，并采用随机抽样一致RANSAC （Random Sample Consensus）算法剔除误匹配点对，解决了SURF算法存在的错误匹配问题。实验结果表明，与SIFT算法、SURF算法相比，本文算法具有更高的匹配精度和更快的匹配速度，且抗旋转、噪声、亮度变化能力更强。
Speed Up Robust Features (SURF) is an improvement of Scale Invariant Feature Transform (SIFT). When it is used to match remote sensing images, SURF can significantly increase matching speed, but slightly decrease matching accuracy. Thus, a remote sensing image matching method based on Non-Subsampled Contourlet Transform (NSCT) and SURF is proposed in this paper. First, the remote sensing image to be matched and the reference image are decomposed by NSCT. Two corresponding low-frequency images are obtained. Then, to reduce the influence of high-frequency noise on matching results, two low-frequency images are inputted to the SURF algorithm to obtain pre-matching results. Finally, to solve the error matching problem of the SURF algorithm, the parameters of the transform model are solved by pre-matching results, and the mismatching is eliminated by using the random sample consensus algorithm. A large number of experiments were conducted, and the results show that compared with the SIFT and SURF algorithms, the proposed algorithm improves the matching speed, as well as the matching accuracy, and exhibits good performance in terms of resisting rotation, noise, and brightness changes.