基于长边缘相关和一致性检测的多传感器图像配准方法

A Fast Method to Remote Image Registration

遥感图像的配准特别是当波段相距较远的图像间配准时,由于其相关性小,直接提取的边缘特征中不一致特征所占比例很大,此时直接应用partialHausdorff距离等方法配准往往失效。本文提出了一种基于长边缘相关的图像配准方法,首先对长边缘进行相关计算,然后在相关长边缘的基础上对其余的边缘进行一致性检测。极大提高了边缘特征的一致性。长边缘相关是在比较HuiLi的相关方法基础上提出的改进Freeman链码相关系数方法。一致性特征检测方法是基于V.Randrianarisoa的检测方法并对之进行了改进。最后对一致边缘的相关部分使用最小二乘法得到了配准参数。仿真实验表明本方法对长边缘丰富的图像有很好的配准结果。并且本方法具有配准速度快的优点。

Some registration approaches can fail when percentage of outliers is too high in remote images. We introduce, in this paper, a new approach to improve the robustness of feature extration for automatic image registration. This method is based on long-edge correlation and consistency check. Long-edge correlation extracts a long edge as reference curve in order to increase the percentage of common feature in the edge maps, and consistent check reduce the number of outliers drastically. The proposed method based on comparison of HuiLi's correlation is a modified chain code correlation coefficient method. In addition, get more consistent-edge by improvement of Randrianarisoa method. The simulation experiments show the robust registration results of the method for images rich in long-edge. Another advantage of the method is rapid computational speed.