用神经网络修正坐标变换中的残差畸变，提升精度与稳定性。
On the Residual-based Neural Network for Unmodeled Distortions in Coordinate Transformation
- 仅学习初始变换后的系统性残差，降低模型复杂度。
- 在稀疏控制点下误差降低18.7%，性能更稳定。
- 适合地理空间数据配准，尤其适用于畸变复杂的场景。
坐标变换模型常无法处理非线性和空间相关畸变，导致地理空间应用中出现显著残差误差。本文提出一种基于残差的神经校正策略，让神经网络仅学习初始几何变换后残留的系统性畸变。通过聚焦残差模式，该方法降低模型复杂度并提升性能，尤其在控制点稀疏或结构化配置时表现更优。我们在模拟数据集（不同畸变强度与采样策略）和真实世界影像地理配准任务中进行评估。相比直接使用神经网络转换器和经典变换模型，该方法在挑战性条件下实现更高精度与稳定性，理想情况下性能相当。结果表明，残差建模是一种轻量且鲁棒的坐标变换精度提升方案。
原文摘要 · Abstract (English)
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.
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