A Study on the Direct Optimization of a Rational Function Model for High-Resolution Satellite Images

Due to the influence of factors such as satellite jitters, orbital errors, star sensor errors, and satellite clock errors, significant geometric systematic errors often exist among multi-view satellite images. This is common for multi-view, cross-orbit satellite data, where complex nonlinear systematic errors are present, making it difficult to correct them using traditional error compensation models. To achieve high-precision block adjustment, this paper proposes a direct adjustment and optimization method for Rational Function Model (RFM) parameters based on prior soft constraints. In this method, the original RFM parameters are used as prior information, which is formulated as prior information soft constraint equations in the adjustment model, aiming at effectively addressing the ill-posed problems. By directly optimizing part or all of the RFM parameters, this method can obtain stable adjustment results in scenarios of complex systematic errors. Experiments among WorldView-3, GaoFen Multi-mode, ZY-3 (Ziyuan-3), and GaoFen-7 satellite data show that, when using multi-view, cross-orbit satellite data and with sufficient and evenly distributed tie points, the proposed full-parameter RFM optimization method and the adaptive RFM optimization method can achieve the highest adjustment accuracy. On the other hand, when using in-track satellite data, the affine systematic error compensation model achieves the highest accuracy, while the adaptive RFM optimization method can achieve comparable accuracy. Therefore, the research results can be applied to intelligent processing scenarios for multi-view, cross-orbit satellite data, such as multi-temporal change detection and multi-view, cross-orbit satellite 3D modeling.