When estimating the disparity of remote sensing images, known phase correlation (PC)-based disparity estimation methods are not fast and robust, such as hierarchical structure PC method and fixed window PC method. To tackle this problem, a parallel PC-based hierarchical framework is proposed, which includes two ideas: first, a weighted PC peak fitting algorithm is introduced for estimating the high precise disparity matrix efficiently and stably; second, a graphics processing unit-based parallel PC algorithm is integrated into the hierarchical framework for fast and robustly estimating high precise disparity map. Additionally, many stages of hierarchical framework, such as padding and reliable evaluation stages, are improved for improving the computational efficiency of disparity estimation system. In a large number of experiments, the results have shown that the efficiency of the proposed algorithm is on average 24 times faster than the compared state-of-the-art methods. Meanwhile, the precision of the proposed algorithm is also superior to or very close to the compared algorithms. The proposed algorithm has been successfully used in a unmanned aerial vehicle three-dimensional retrieval system, and the practice effect has also been verified.

当估计遥感图像的视差时，已知​​的基于相位相关（PC）的视差估计方法不是快速且健壮的，诸如分层结构的PC方法和固定窗口的PC方法。为解决这一问题，提出了一种基于并行PC的分层框架，其中包括两个思想：首先，引入加权PC峰值拟合算法，以高效，稳定地估计高精度视差矩阵。其次，将基于图形处理单元的并行PC算法集成到分层框架中，以快速，稳健地估计高精度视差图。另外，改进了分级框架的许多阶段，例如填充和可靠的评估阶段，以提高视差估计系统的计算效率。在大量实验中 结果表明，所提出算法的效率平均比所比较的最新方法快24倍。同时，所提算法的精度也优于或非常接近比较算法。所提出的算法已成功应用于无人机三维检索系统，并已验证了其实际效果。