RPC Correction Coefficient Extrapolation for KOMPSAT-3A Imagery in Inaccessible Regions

Highlights: What are the main findings? This study proposes a transport-based RPC correction learned on a small head subset, extrapolating downstream while preserving geometry and yielding <3-pixel tails in two of three strips. This study models pushbroom error extrapolation by leveraging satellite orbital parameters and terrain characteristics to transport head-of-strip corrections downstream. What is the implication of the main finding? This study provides a practical alternative to strip-wide block adjustment for control-denied or resource-limited settings, operating in image space and tolerating missing segments or ties. This study offers a transferable framework for sub-meter platforms; under stronger dynamics, broader calibration and optional higher-order terms further stabilize transport. High-resolution pushbroom satellites routinely acquire multi-tenskilometer-scale strips whose vendors’ rational polynomial coefficients (RPCs) exhibit systematic, direction-dependent biases that accumulate downstream when ground control is sparse. This study presents a physically interpretable stripwise extrapolation framework that predicts along- and across-track RPC correlation coefficients for inaccessible segments from an upstream calibration subset. Terrain-independent RPCs were regenerated and residual image-space errors were modeled with weighted least squares using elapsed time, off-nadir evolution, and morphometric descriptors of the target terrain. Gaussian kernel weights favor calibration scenes with a Jarque–Bera-indexed relief similar to the target. When applied to three KOMPSAT-3A panchromatic strips, the approach preserves native scene geometry while transporting calibrated coefficients downstream, reducing positional errors in two strips to <2.8 pixels (~2.0 m at 0.710 m Ground Sample Distance, GSD). The first strip with a stronger attitude drift retains 4.589 pixel along-track errors, indicating the need for wider predictor coverage under aggressive maneuvers. The results clarify the directional error structure with a near-constant across-track bias and low-frequency along-track drift and show that a compact predictor set can stabilize extrapolation without full-block adjustment or dense tie networks. This provides a GCP-efficient alternative to full-block adjustment and enables accurate georeferencing in controlled environments.