总体最小二乘用于线阵卫星遥感影像光束法平差解算

顾及像点观测方程的系数矩阵中存在随机误差,提出了基于总体最小二乘的线阵卫星遥感影像光束法平差模型。在假定像点观测误差和系数矩阵误差均为独立、等精度分布的基础上,利用拉格朗日条件极值法推导了包含外方位元素虚拟观测方程和控制点误差方程的总体最小二乘光束法平差算法的具体公式和计算方法。该方法利用方差分量估计确定各类虚拟观测值的方差,可求解包含多类虚拟观测量的平差问题,并可用先验信息或岭迹法确定系数矩阵观测值的权比例系数,从而克服了现有总体最小二乘虚拟观测方法不能处理多类虚拟观测值的不足,确保了光束法平差可正确有效求解。分别利用模拟算例与两组真实影像进行了试验验证。结果表明,相比于常规最小二乘虚拟观测法以及现有总体最小二乘虚拟观测方法,本文方法具有更高的求解精度与适应性。相较于传统线阵卫星遥感影像光束法平差方法,本文方法可以获得更高的平差计算精度。

Bundle Adjustment for Satellite Linear Array Images Based on Total Least Squares

Since the coefficient matrix of image point observation equations may contain random error, it is proposed that a bundle adjustment method for satellite linear array CCD imagery based on total least squares. Assuming that both point observation random error and the coefficient matrix random error are independent and identically distributed, a total least squares based bundle adjustment algorithm, which contains both virtual observation equations of exterior elements and error equations of ground control points, has been deduced using Lagrange conditional extremum. The variance of any type of virtual observation equation can be estimated using variance component estimation. Thus the proposed method can handle with adjustment problems with more than one type of virtual observation equation and chose the undetermined coefficient in proposed method using priori information or the ridge mark method, which overcomes the deficiency of existing virtual observation total least squares method and ensures that the adjustment problems can be solved correctly and effectively. Experiments have been taken on both simulative data and real satellite linear array images in two areas. Results indicate that the proposed method can get more accurate solutions than traditional least squares algorithm and recently proposed virtual observation total least squares method. The proposed method can also get more accurate bundle adjustment results when compared to conventional method.