A stable block adjustment method without ground control points using bound constrained optimization

Block adjustment is a key technology for achieving large-scale accurate mapping of space via remote sensing. When block adjustment without ground control points (GCPs) is conducted, the adjustment model has large condition number of design matrix, and an ill-conditioned normal equation, which eventually leads to abnormal convergence of direct least squares solution. In this study, we investigated block adjustment without GCPs based on Rational Function Model (RFM), and applied image space affine model of RFM as the systematic error compensation model of single scene imagery. The value range of each parameter of the affine model was determined by calculating the inverse projection errors of tie points and the adjustment model parameters were solved by the optimization method with attached inequalities constraint. Finally, the abnormal convergence problem was solved by reducing the degree of freedom of convergence. Comparative tests of block adjustment without GCPs using various methods were conducted using multiple regional imagery from Ziyuan-3 (ZY-3), Tianhui-1 (TH-1), Luojia-1 (LJ-1) and Gaofen-3 (GF-3). The results show that the proposed method can achieve rapid and correct convergence, and has strong practicability.

Automated summary

In this study, we propose a method for block adjustment without ground control points, based on the Rational Function Model (RFM). By using the image space affine model of RFM as the systematic error compensation model, we solve the abnormal convergence problem and achieve rapid and correct convergence. Comparative tests using multiple regional imagery validate the effectiveness and practicality of the proposed method.