On the Residual-based Neural Network for Unmodeled Distortions in Coordinate Transformation

TL;DR

This paper introduces a residual-based neural network approach to correct unmodeled distortions in coordinate transformations, enhancing accuracy and robustness in geospatial applications especially with sparse data.

Contribution

It presents a novel residual learning strategy that models systematic distortions separately, reducing complexity and improving performance over traditional methods.

Findings

Outperforms classical models in distorted scenarios

Maintains accuracy with sparse control points

Provides stable results across various distortions

Abstract

Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions, leading to significant residual errors in geospatial applications. Here we propose a residual-based neural correction strategy, in which a neural network learns to model only the systematic distortions left by an initial geometric transformation. By focusing solely on residual patterns, the proposed method reduces model complexity and improves performance, particularly in scenarios with sparse or structured control point configurations. We evaluate the method using both simulated datasets with varying distortion intensities and sampling strategies, as well as under the real-world image georeferencing tasks. Compared with direct neural network coordinate converter and classical transformation models, the residual-based neural correction delivers more accurate and stable results under challenging conditions, while maintaining comparable performance in ideal cases. These findings demonstrate the effectiveness of residual modelling as a lightweight and robust alternative for improving coordinate transformation accuracy.