图像配准是图像融合、变化检测、目标识别等遥感应用中的重要步骤。互信息由于具有无需预处理、自动化程度高以及鲁棒性强等特点，将其作为一种相似性测度进行图像配准成为近几年图像处理领域的研究热点。随着遥感图像数据量的不断加大，传统的单机处理模式已经无法满足一些应用的时效性要求。基于对串行算法计算瓶颈的实验分析，研究并提出了一种基于互信息的遥感图像区域配准并行算法，分别给出了数据划分策略和互信息计算并行处理方案，采用边界冗余划分和二叉树归约方法减少数据通信，并对算法进行了定量的复杂度分析。实验结果表明该算法可扩展性

Image registration is an important step of image fusion, change detection and target recognition in remote-sensing applications. As a similarity measure, mutual information has become a hot topic in image processing, for its advantages of no pre-processing, robustness and a high degree of automation. But with the increasing of image size in remote-sensing, high computing complexity of image registration algorithm makes that traditional single-processor computing mode could not meet the requirement of real-time processing in some applications. Based on the experimental analysis of corresponding serial algorithm, a parallel region-based registration algorithm based on mutual information for remote-sensing images is proposed. Strategy of data partition and parallel computing method of mutual information are given. To avoid data communication, some techniques like redundant data partition on boundary and reducing on binary-tree are used. Then, a quantitative analysis of computing difficulty is given. The experimental results show that the proposed parallel algorithm has good scalability and applicability.