针对基于点特征的遥感图像自动配准算法中存在特征点分布不均匀的问题, 提出了一种基于 SIFT(尺度不变特征变换 )、Harris-Laplace(多尺度角点 )、MSER(最大稳定极值区域 )特征提取算法的多特征遥感影像配准方法。通过多特征与二次匹配, 极大的提高了匹配点数目；通过基于距离的筛选, 保证匹配点分布均匀合理；通过局部互信息精校正, 使匹配点精度更高, 最终达到高质量 (空间分布均衡, 匹配精度高 )自动配准目的。

Uniformity in point features distribution of feature extraction algorithm at remote sensing image automated registration is a challenging problem. For the purpose of remote sensing image automated registration, a multi-feature image automated registration algorithm is presented based on Scale Invariant Feature Transform (SIFT), Harris-Laplace and Maximally Stable Extremal Region (MSER). Multi-feature and secondary matching greatly promotes the number of matching points, screening by distance makes matching points distribute uniformly and reasonably, and local mutual information takes matching points more accurate. Finally, a high quality (uniform space distribution, accuracy matching points) automated image registration is achieved.