Based on 1 1 Norm Constrained Rational Function Model Parameter Solving and Full Parameter Optimization Method

Abstract

Disclosed are L<1> norm constraint based parameter solution and all-parameter optimization methods for a rational function model. In a terrain-independent scheme, when a satellite image has large internal distortion, reliable parameters of the rational function model can still be obtained; in a terrain-dependent scheme, 78 rational function model coefficients can be solved with fewer than 39 control points. Under the condition of known rational function model coefficients, the rational function model is subjected to all-parameter optimization with few ground control points. The L<1> norm constraint based parameter solution and all-parameter optimization methods for the rational function model have the advantages that stability of parameter solution for the rational function model is improved, fewer control points are required, reliable solutions for the parameters of the rational function model can be obtained with fewer than 39 control points, and precision of fine image correction is improved.

Description

technical field [0001] The invention relates to a method for solving and optimizing parameters of a rational function model, which can self-adaptively simplify the rational function model, reduce the number of required control points, and improve image correction precision. It can be applied to remote sensing, photogrammetry, surveying and mapping, image processing and other fields. Background technique [0002] Rational Function Model (RFM) is a general-purpose geometric imaging model, which has good interpolation characteristics and continuity and is independent of sensors and platforms. In addition, it also has high fitting accuracy, imaging parameters and model confidentiality, Due to the advantages of fast calculation speed, it is widely used in the geometric correction of satellite remote sensing images. [0003] The rational function model expresses the image point coordinates as the ratio of rational polynomials with the corresponding ground point space coordinates ...

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Problems solved by technology

[0034] Although the rational function model has many advantages, there are still some disadvantages: 1) the model contains 78 RPCs, and at least 39 control points are needed to solve it; 2) there is a strong correlation between the 78 RPCs, which makes the RPCs 3) The rational function model is a purely mathematical model and lacks geometric meaning; 4) The rational function model is very successful in high-resolution images with narrow field of view, but for wide field of view and orientation In the case of unstable parameters, the applicability is poor

These methods can improve the ill-conditionedness of the equation to a certain extent and prevent "overfitting" phenomenon, but still need a large number of control points to solve RPCs

When the number of control points is not large enough, the obtained solution is often very unstable

In addition, for domestic satellites such as HJ-1A / B, due to the large sensor field of view and unstable platform attitude, the internal distortion of the image is relatively large, and the phenomenon of model instability is more likely to occur when solving RPCs. Accurate correction is difficult to achieve through simple image square correction, which is why the rational function model cannot be applied to this type of satellite remote sensing image

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