异质图像配准是多源图像融合的关键步骤之一，通常需要精确提取和匹配图像的同名特征，这种同名特征在成像机理差异巨大的光学和SAR图像中进行提取和匹配十分困难，利用相同场景图像中的隐含相似性可以有效避开这一难点。为了对光学和SAR图像进行配准，提出了一种基于隐含相似性的光学和SAR图像配准方法，该算法首先选用高梯度幅值像素作为隐含特征点集，然后通过像素迁移来构建相似测度准则函数，并用遗传算法对准则函数解空间进行全局优化搜索来获取配准解，这样就将图像配准问题归于模型参数优化求解过程。实验结果表明，该方法有效可行，配准图像能达到像素级配准精度。

The registration of heterogeneous images is one of the most important steps of multi-resource image fusion.Heterogeneous image registration algorithms need to extract and match the invariant feature precisely,which can be difficult due to huge differences between optical and SAR imaging principles.This problem can be avoided by introducing implicit similarity existing in the same scenes.The algorithm in this paper constructs the implicit features by large scale pixels and then similarity criterion function is constructed using pixels migration.The registration results can be obtained by optimizing criterion function through genetic algorithm(GA)’s global optimum solution.Experimental results show the registration accuracy of this algorithm achieves pixel level.