一种稳健的多源遥感图像特征配准方法

 提出了一种稳健的多源遥感图像特征配准方法.首先引入相位一致性变换来消除图像灰度、对比度差异的影响并提取特征点;然后基于Zernike矩重构互相关函数并通过重构函数最大化实现图像旋转条件下的特征点匹配,同时结合双向最大互相关值匹配策略实现图像的粗略配准;最后,采用迭代修正变换参数算法最终实现多源遥感图像高精度配准.对大量多源图像进行配准实验表明,提出方法对多源遥感图像配准具有较强的适应性且配准精度高.

A Robust Multi-Source Remote-Sensing Image Registration Method Based on Feature Matching

A robust feature based image registration method is proposed for multi-source remote-sensing images.Firstly,phase congruency transform is used to compute phase information images for removing the influence of the great differences in gray-level or contrast and to extract the control points.Then,the cross correlation function is reconstructed by Zernike moment and maximized to match the control-point pairs under rotation condition.Simultaneously,the initial feature point pairs are gained with the matching strategy of the bidirectional greatest cross correlation coefficient.Lastly,an iterative refined parameters method is used to improve the accuracy of registration.Experimental results demonstrate that this novel method can efficiently offer a reliable,robust and accurate image registration for the multi-source remote-sensing images.