Commission I, WG I/4KEYWORDS: satellite, image, metadata, accuracy, correlation, extraction, estimation, triangulation
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 3x3 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 0.9p vertical errors to measured 0.9p 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 0.9p 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.
ABSTRACT:Sensor builders in the digital era have design limitations due to the constraint of maximum available digital array size. A straightforward solution exists, for example, when four cameras that each simultaneously captures an image from essentially the same perspective centre; they can be re-sampled to form a virtual large format image that can be exploited using a single (instead of four separate) instantiation of a frame model. The purpose of this paper is to address the less trivial time-dependent cases where the sensor scans the ground and the detector arrays obtain chips of imagery that need to be stitched together to form a single conveniently exploitable image. Many operational techniques warp the imagery to form a mosaic, or ortho-rectify it using an imperfect digital surface model (DSM), thus eliminating the possibility for accurate geolocation and uncertainty estimation. This algorithm, however, forms a single virtual image with associated smooth metadata, which can be exploited using a simple physical sensor model. The algorithm consists of four main steps: 1) automated tie point matching; 2) camera calibration (once per sensor); 3) block adjustment; and 4) pixel re-sampling based on an "idealized" virtual model. The same geometry model used to form the image, or its true replacement, must be used to exploit it. This paper verifies the algorithm using real imagery acquired from the Global Hawk (GH) UAV. Registration of the virtual image to a WorldView1 stereopair using four tie points yielded an RMS below 0.6 meters per horizontal axis.
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