A Coarse-to-Fine Geometric Calibration Framework of RPCS for Remote Sensing Images

The development of Earth Observation technology makes it possible for clearly understanding of our planet. Very high resolution (VHR) satellite remote sensing images with sub-meter level has been applied in many fields including land monitoring, urban construction and others. Traditional models focus on the improvement of the over-all accuracy of processed images, while local distortions leading pixel-level geolocation error has not attracted attention for VHR images. Hence, this paper focus on the calibration of local distortions between images with a coarse-to-fine framework. Benifited from the widespread of the rational function model (RFM), the affine transformation model was utilized for the overall accuracy improvement using sparse matching points. Then, dense matching relationships are filled to build a fine calibration model based on the error distribution between images. Images obtained from the Chinese Gaofen-2 (GF-2) are experimented. Results indicated that our proposed method can significantly improvement the geometric consistency between VHR images, providing a generic way for geometric process of VHR images.