Auto-calibration of GF-1 WFV images using flat terrain

Abstract

Four wide field view (WFV) cameras with 16-m multispectral medium-resolution and a combined swath of 800 km are onboard the Gaofen-1 (GF-1) satellite, which can increase the revisit frequency to less than 4 days and enable large-scale land monitoring. The detection and elimination of WFV camera distortions is key for subsequent applications. Due to the wide swath of WFV images, geometric calibration using either conventional methods based on the ground control field (GCF) or GCF independent methods is problematic. This is predominantly because current GCFs in China fail to cover the whole WFV image and most GCF independent methods are used for close-range photogrammetry or computer vision fields. This study proposes an auto-calibration method using flat terrain to detect nonlinear distortions of GF-1 WFV images. First, a classic geometric calibration model is built for the GF1 WFV camera, and at least two images with an overlap area that cover flat terrain are collected, then the elevation residuals between the real elevation and that calculated by forward intersection are used to solve nonlinear distortion parameters in WFV images. Experiments demonstrate that the orientation accuracy of the proposed method evaluated by GCF CPs is within 0.6 pixel, and residual errors manifest as random errors. Validation using Google Earth CPs further proves the effectiveness of auto-calibration, and the whole scene is undistorted compared to not using calibration parameters. The orientation accuracy of the proposed method and the GCF method is compared. The maximum difference is approximately 0.3 pixel, and the factors behind this discrepancy are analyzed. Generally, this method can effectively compensate for distortions in the GF-1 WFV camera.