用自适应特征匹配提升多时相卫星图像的几何校准精度
Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences
- 结合学习型局部特征与全局描述子，实现跨季节对应点匹配
- 在39-42视图数据集上，几何一致性误差更低且匹配耗时显著减少
- 适合无控制点、多时相卫星影像处理场景，尤其应对季节变化
高精度理性多项式相机（RPC）模型校准对高质量卫星影像定位至关重要。在无地面控制点的多视角流程中，通常通过自动提取的图像对应点进行束调整来实现校准。然而，传统方法依赖手工特征匹配，在受季节、光照和地表覆盖变化影响的多时相影像中表现不可靠。本文提出一种外观感知的RPC精化流程，融合学习型局部特征匹配以获取季节不变对应点，并利用全局图像描述子筛选视觉兼容的图像对，从而减少冗余与错误匹配，同时保持匹配图连通性。在具有季节多样性的WorldView-3影像数据集上的实验表明，该方法在无控制点条件下优于开源基线，相对RPC校准精度更高，几何一致性误差更小，且在39-42视图的影像集合上显著降低匹配时间。该方法提升了对时序外观变化的鲁棒性，使多时相卫星影像得以更有效利用。
原文摘要 · Abstract (English)
Accurate refinement of Rational Polynomial Camera (RPC) models is essential for high-quality satellite image geolocation. In ground control point (GCP)-free multi-view pipelines, this refinement is commonly performed through bundle adjustment from automatically extracted image correspondences. However, conventional RPC bundle adjustment pipelines rely on handcrafted feature matching, which becomes unreliable in multi-date collections affected by seasonal, illumination, and land-cover changes. We propose an appearance-aware RPC refinement pipeline that combines learned local feature matching for season-invariant correspondences with global image descriptors for selecting visually compatible image pairs. This reduces redundant and error-prone matching while preserving the connectivity of the matching graph. Experiments on seasonally diverse WorldView-3 images show that our pipeline improves GCP-free relative RPC refinement over open-source baselines, achieving lower geometric consistency errors while substantially reducing matching time on collections with 39-42 views. By making RPC refinement more robust to diachronic appearance variation, our approach enables more effective use of multi-date satellite imagery.
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