基于SURF特征提取的遥感图像自动配准

基于SURF（Speeded UpRobust Features）特征点提取是目前比较流行的图像配准方法．本文在SURF基础上，提出一种基于分块策略的改进方法：首先采用分水岭分割法确定图像的分块数量，然后对图像进行分块，每个子块提取一定数量的特征点，以便实现特征点的均匀提取；再通过稀疏特征树法找出匹配的特征点对；最后用RANSAC算法剔除错误匹配特征点对，同时计算参考图像与待配准图像的变换关系．实验表明，该方法能够高效、快速地解决遥感图像的自动配准问题．

Remote Sensing Image Automatic Registration Based on SURF Feature Extraction

SURF (Speeded Up Robust Features) feature extraction is currently more popular image registration method. This paper proposed a improved method based on block strategy on the basis of SURF. Firstly, using Watershed Algorithm to determine the number of image blocks; then the image was divided into blocks and each sub-block extracted a certain amount of feature points to realize uniform feature point extraction; then using sparse feature tree to find the matching feature points and finally using improved RANSAC algorithm to eliminate the error matching feature point pairs, while calculating transformation between the reference image and the image to be registered. Experiments show that this method can efficiently and quickly solve the problem of remote sensing image automatic registration.