Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences

2026-07-29

AI Summary

This study addresses the challenge of unreliable manual feature matching in traditional RPC bundle adjustment for multi-temporal satellite imagery, which suffers from seasonal variations, illumination changes, and surface cover dynamics that degrade uncontrolled geometric positioning accuracy. To overcome this limitation, the authors propose an appearance-aware RPC refinement method that, for the first time, jointly leverages learned local features and global image descriptors to robustly extract season-invariant correspondences. Furthermore, the approach employs visual compatibility metrics to select optimal image pairs, thereby enhancing the match graph structure. Evaluated on a multi-season WorldView-3 dataset, the method significantly outperforms open-source baselines, achieving notably reduced geometric consistency errors and substantially improved matching efficiency across image blocks of 39–42 scenes.

Abstract

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.

Problem

• RPC bundle adjustment
• multi-date satellite imagery
• season-invariant correspondences
• feature matching
• geolocation accuracy

Innovation

• RPC bundle adjustment
• season-invariant correspondences
• learned local features
• global image descriptors
• multi-date satellite imagery