一种快速有效的大数据区域网平差方法

针对摄影测量影像来源多样化、复杂化、大数据化等趋势,传统区域网平差算法在应对当前复杂多变的数据来源,矩阵排列毫无规律的法方程结构以及大数据量带来的高内存需求和低计算效率等问题上,遇到了前所未有的挑战,为了解决上述难题,本文引入了预条件共轭梯度法以及不精确牛顿解法求解区域网平差过程中的法方程,同时使用一种块状法方程系数矩阵压缩存储格式,构建了全新的区域网平差技术流程。本文方法避免了直接对法方程系数矩阵的求逆,压缩了法方程系数矩阵所需的内存空间,使得本文算法比传统算法所需计算机内存空间大幅减少,平差计算速度明显提升,同时保证了计算精度与传统方法相当。初步试验证明,本文方法对4500张影像、近900万像点数据的平差计算在普通电脑上仅需要约15min,且计算精度达到子像素级。

A Fast and Effective Block Adjustment Method with Big Data

To deal with multi-source,complex and massive data in photogrammetry,and solve the high memory requirement and low computation efficiency of irregular normal equation caused by the randomly aligned and large scale datasets,we introduce the preconditioned conjugate gradient combined with inexact Newton method to solve the normal equation which do not have strip characteristics due to the randomly aligned images.We also use an effective sparse matrix compression format to compress the big normal matrix,a brand new workflow of bundle adjustment is developed.Our method can avoid the direct inversion of the big normal matrix,the memory requirement of the normal matrix is also decreased by the proposed sparse matrix compression format.Combining all these techniques,the proposed method can not only decrease the memory requirement of normal matrix,but also largely improve the efficiency of bundle adjustment while maintaining the same accuracy as the conventional method.Preliminary experiment results show that the bundle adjustment of a dataset with about 4500 images and 9 million image points can be done in only 15 minutes while achieving sub-pixel accuracy.