Abstract
High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D) geo-positioning of the bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs) with respect to the high-resolution QuickBird stereo images in three spaces (i.e., orbital space, image space and object space). The compared models include a bias-corrected RSM in the orbital space, a bias-corrected RSM and RFM in the image space, and a bias-corrected RSM and RFM in the object space. In the comparison, the RSMs and RFMs use the vendor-provided orbit data and Rational Polynomial Coefficients (RPCs), respectively. The experimental results indicated that, (1) these five bias-corrected models can provide a sub-pixel geo-positioning accuracy. With the zero-order polynomial correction model in the orbital space and a minimum of three Ground Control Points (GCPs), the accuracy based on RPCs better than 0.8 m in horizontal direction and 1.3 m in vertical direction. With an increase in the number of GCPs, or in the order of correction models, the regenerated orbital parameters achieve a slight improved positioning accuracy of 0.5 m in horizontal direction and 0.8 m in vertical direction with 25 GCPs, which indicates that the low-order correction model in the orbital space can accurately model the effects of ephemeris and attitude errors; (2) the performances of bias-corrected RSM and RFM in image space are rather similar. However, the bias-corrected RSM and RFM in image space achieve a better accuracy than the bias-corrected RSM and RFM in object space, with the same configuration of GCPs.
Highlights
The rapid development of High-Resolution Satellite Imagery (HRSI), such as QuickBird and IKONOS, has provided a large number of applications in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction with a sub-meter spatial resolution
Each bias-correction model as introduced in Section 3.2 was performed based on the minimum number of Ground Control Points (GCPs), and additional GCPs were added to evaluate the influence of GCPs configuration on the geo-positioning accuracy of bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs)
The geo-positioning accuracies of QuickBird stereo imageries based on non-corrected RSM are 12.398 m in horizontal direction and 21.158 m in vertical direction, and those based on non-corrected RFM are 12.524 m and 21.186 m, respectively
Summary
The rapid development of High-Resolution Satellite Imagery (HRSI), such as QuickBird and IKONOS, has provided a large number of applications in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction with a sub-meter spatial resolution. The RSM includes interior and exterior orientation parameters of the acquired image [4,5] The former parameters can be interpreted directly from the vendor-provided image metadata, and the latter need to be interpolated by both ephemeris data and attitude data of the satellite, by the use of least squares adjustment [6] or Lagrange interpolation [7]. The RFM is a generalized sensor model that uses the ratio of polynomials and is regarded as an alternative to the RSM [10]. This model has the benefit of low computational complexity, a closed form solution, and equivalent accuracy to the RSM [11]
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