大幅面多光谱遥感图像快速自动配准

针对大幅面多光谱遥感图像的配准需求,提出一种基于特征点的快速全自动配准方法。由于多光谱遥感图像的尺寸较大,计算量大,因此提出特征网格理论,即根据图像灰度值、信息熵值及特征分布均匀性准则,在二级规则网格中选取特征网格参与后续运算,以减小计算量。同时,该理论为SIFT(Scale Invariant Feature Transform)特征点提取算法的并行运行及特征点初匹配方法的改进提供了条件,提高了算法的效率及配准精度。利用本算法对CBERS-02B拍摄的遥感图像进行了实验。结果表明,该方法能够达到亚像素级配准精度,且计算速度快,能够满足大幅面遥感图像处理的要求。

Fast and Automatic Registration Method for Large Multi-spectral Remote Sensing Images

Aiming at the registration of large multi-spectral remote sensing images,a fast and automatic registration method based on feature points was proposed.Because the size of the multi-spectral remote sensing image is quite large,and the amount of calculation is also very great,the theory of feature grid based on grey value,entropy and uniformity principle was proposed.Feature grids chosen from a two-degree regular mesh are calculated in the subsequent process to reduce the calculation.Meanwhile,the theory provides condition for detecting SIFT(Scale Invariant Feature Transform) feature points parallelly,and for improving the primary feature matching step,so the efficiency and accuracy are increased.The proposed method was applied to remote sensing images taken by CBERS-02B.The experimental results with large remote sensing images clearly indicate that the proposed approach can achieve sub-pixel precision,decrease the runtime of the process,and the requirement of large remote sensing image process is satisfied.