Cross-Scene Transfer Accuracy of Geometric Correction Parameters for GF-3B/C SAR Imagery
Year: 2026
Type: article
Abstract: Accurate geometric positioning is essential for the quantitative application of synthetic aperture radar (SAR) imagery. Although geometric calibration can effectively reduce systematic positioning errors, the cross-scene transferability of calibration-derived correction parameters remains insufficiently evaluated, particularly under different acquisition conditions. This study investigates the geometric positioning accuracy and parameter transferability of GF-3B/C SAR imagery using observations from the Xilinhot and Lijiang calibration sites. Scene-specific range and azimuth-time corrections were first estimated within the Range–Doppler framework, followed by cross-scene transfer experiments and acquisition-condition sensitivity analyses. The results indicate that scene-specific correction established a high-accuracy baseline, reducing the median planimetric root-mean-square error (RMSE) from 57.50 m to 1.53 m. In cross-scene transfer, the range correction exhibited substantially higher stability than the azimuth-time correction. Range-only transfer yielded a median combined two-dimensional RMSE of 50.98 m, whereas combined transfer yielded 60.51 m, compared with 57.85 m before transfer. Transfer performance was associated with source–target acquisition compatibility, yielding superior accuracy when satellite platform, orbit direction, and Doppler centroid frequency differences were well matched. Furthermore, cross-site transfer demonstrated accuracy comparable to that of same-site transfer under compatible conditions. These findings indicate that parameter transferability depends on both the specific correction component and acquisition compatibility. For GF-3B/C imagery in ground-control-point-free (GCP-free) scenarios, range corrections offer strong potential for direct reuse, whereas azimuth-time corrections require rigorous compatibility screening during source-scene selection.
Source: Remote Sensing
DOI: 10.3390/rs18213845
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7225642201