Abstract
Abstract. Precise geo-positioning of high-resolution optical satellite imagery without ground control points (GCPs) has always been the goal pursued by photogrammetry scholars. This paper introduces the block adjustment (BA) without GCPs based on rational function model (RFM) model and its practical application in high-precision geo-positioning of optical satellite imagery. The mainly key technologies of BA model construction based on virtual control points (VCPs), gross error detection and elimination, and GPU parallel computing method of large-scale adjustment are studied. On this basis, experimental analysis and validation of 123 images of ZY-3 satellite in Taihu are carried out. The results show that the sparse matrix compression can reduce the memory requirement effectively. The GPU parallel computing can solve the problem of large-scale BA computational efficiency. In addition, after BA, the maximum residual is 3.79 pixel, the root mean square error (RMSE) is 0.37 pixel in the x (flight) direction, the maximum residual error is 7.18 pixel, and the RMSE is 0.66 pixel in the y (scan) direction. The proposed method has certain accuracy and stability in large-scale BA without GCPs. The relative positioning accuracy can reach sub-pixel level, which could meet the requirements of cartographic mosaicking.
Highlights
Under the background of global mapping, regions with little or no control point information, such as the plateau, alpine and gorge, desert and other areas without available ground control points (GCPs), are often encountered (Wang et al, 2017)
Yao et al (2018) adopted the block adjustment (BA) method based on satellite images covered by multipletimes in the same place, which improved the accuracy of geopositioning of satellite images without GCPs
The geo-positioning accuracy of the BA of 8802 ZY-3 three-line array stereo images without GCPs covering the scope of China was better than 5 m (Wang et al, 2017)
Summary
Under the background of global mapping, regions with little or no control point information, such as the plateau, alpine and gorge, desert and other areas without available ground control points (GCPs), are often encountered (Wang et al, 2017). Reducing the dependence on GCPs is to reduce cost and improve efficiency, which is efficient and effective method, and no GCPs and large-scale are the development trend of optical satellite imagery adjustment technology in the future (Wang et al, 2017). In the case of sparse GCPs, BA experiments were carried out by using the constraint between different strip images, the influence of systematic error of RPC model was corrected effectively (Li et al, 2006). Zhou et al (2016) used SRTM data to interpolate the elevation value of object points corresponding to tie points as the initial adjustment value, which improved the image elevation positioning accuracy of large-scale BA effectively. The geo-positioning accuracy of the BA of 8802 ZY-3 three-line array stereo images without GCPs covering the scope of China was better than 5 m (Wang et al, 2017)
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