最大化算术-几何均值距离的多传感器遥感图像配准

互信息是多模态医学图像配准的一种重要方法。它测量的是两个概率分布之间的Kullback-Leibler(KL)距离。该文分析了KL距离和Shannon不等式之间的关系，在此基础上，提出了一种新的算术-几何均值距离，并将这一距离测度用于多传感器遥感图像的配准处理。与Kullback-Leibler距离不同，新的距离测度具有对称性，并且对概率值为0的情况不需要特殊处理。文中首先通过一维仿真信号对算术-几何(AG)测度进行了分析，并使用Thematic Mapper (TM), Satellite POsitioning and Tracking (SPOT)遥感图像和雷达图像进行了配准实验，验证了提出的新的算术-几何均值距离函数在配准多传感器遥感图像方面的有效性。与目前常用的相关系数的方法不同，这种新方法对于像素灰度值不具有线性关系的多传感器遥感图像能够实现配准处理。

Multi-sensor Remote Sensing Image Registration by Maximization of Arithmetic-Geometric Mean Divergence

Mutual information is an important method for multimodal medical image registration. It measures Kullback-Leibler (KL) divergence between two probability distributions. The connection between KL divergence and Shannon inequality is investigated. Base on the connection, a novel measure, Arithmetic-Geometric (AG) mean divergence, is proposed. It can be used for alignment of remote sensing images acquired by different sensors. Unlike KL divergence, the new measure is symmetry and do not require the condition of absolute continuity to be satisfied by the probability distribution involved. AG divergence measure is applied to one-dimensional simulated signals, and to affine registration of Thematic Mapper (TM), Satellite Positioning and Tracking (SPOT) and Synthetic Aperture Radar (SAR) remote sensing images. The performance of AG divergence measure is validated by experiments. The results show that AG measure do not require the approximate linear relation of pixel intensity value in image pairs, and is practicable even though the gray values of images are much different from each other.