Abstract
Abstract. Extraction of ground points using a basic stereo pair of commercial satellite electro-optical images typically yields vertical errors much smaller than expected. In particular, the magnitude of vertical errors relative to horizontal errors is significantly smaller than expected based on imaging geometry (convergence angle, etc.) alone. This paper suggests that temporal correlation or similarity of metadata (sensor position, attitude) errors between two same-pass images is the major cause of this phenomenon. It discusses the sources of temporal correlation, how it can be represented, and how an optimal ground point extraction algorithm detailed in the paper uses this representation in order to provide the best possible 3D location and corresponding 3 x 3 error covariance for reliable predicted solution accuracy. This paper also provides an estimate for temporal correlation, approximately 0.70 (70%), and explains how this value was derived based on the ratio of measured 0.9 p vertical errors to measured 0.9 p horizontal errors compiled over many stereo pairs and ground truth points as described in various papers in the literature. As demonstrated in this paper, based on simulation and error propagation for typical stereo geometry, if this correlation is not accounted for, predicted 0.9 p vertical error is approximately 60% too large. Knowledge of temporal correlation is essential for reliable stereo accuracy prediction as well as proper modeling of a priori metadata uncertainty in the support of metadata adjustment in a value-added process, such as registration to sparse control or a block adjustment.
Highlights
1.1 Sensor Support Data and Corresponding ErrorsThe sensor support data for commercial satellite electro-optical imagery consists primarily of a time series of sensor position and attitude
The support data for a stereo pair of images taken on the same-pass is temporally correlated, which affects the accuracy of stereo extraction, just as imaging geometry affects accuracy, as will be demonstrated later
(Dolloff, 2004) discusses the need and methods to account for temporal correlation in optimal geopositioning algorithms, including Weighted-Least Squares (WLS) stereo extraction, but does not present quantitative results based on real-world data
Summary
The sensor support data (image metadata) for commercial satellite electro-optical imagery consists primarily of a time series of sensor position and attitude. This data is required for the extraction of a ground point identified and measured in an image (mono extraction) or in a stereo pair of images (stereo extraction). The above description is relative to a physical sensor model and its support data; support data for a corresponding rational polynomial coefficients (RPC) sensor model consists primarily of a ground-to-image polynomial. Overall conclusions are applicable to both sensor models, since one is derived from the other
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