Comparison of two physical sensor models for satellite images: Position–Rotation model and Orbit–Attitude model

The main objective of this paper is to compare two types of physical sensor models of linear pushbroom satellite images: one that uses position and rotation angles as model parameters and one that uses orbit and attitude angles as model parameters. Comparison is carried out by two accuracy measures: the accuracy of bundle adjustments and the accuracy of estimating exterior orientation parameters. The first measure has been used widely to indicate the mapping accuracy of sensor models. It is argued that the second measure is also important for certain applications. The two types were implemented with different sets of unknown parameters and tested with two KOMPSAT-1 Earth Observing Camera (EOC) scenes and GPS-derived control points. In terms of the first measure the two models produced similar results whereas in terms of the second measure the one based on orbit and attitude outperformed the other. It seems better to use this model if one wishes to retrieve satellite orbit or attitude through bundle adjustments.     

DEM matching for bias compensation of rigorous pushbroom sensor models

DEM matching is a technique to match two surfaces or two DEMs, at different reference frames. It was originally proposed to replace the need of ground control points for absolute orientation of perspective images. This paper examines DEM matching for precise mapping of pushbroom images without ground control points. We proved that DEM matching based on 3D similarity transformation can be used when model errors are only on the platform’s position and attitude biases. We also proposed how to estimate bias errors and how to update rigorous pushbroom sensor models from DEM matching results. We used a SPOT-5 stereo pair at ground sampling distance of 2.5 m and a reference DEM dataset at grid spacing of 30 m and showed that rigorous pushbroom models with accuracy better than twice of the ground sampling distance both in image and object space have been achieved through DEM matching. We showed further that DEM matching based on 3D similarity transformation may not work for pushbroom images with drift or drift rate errors. We discussed the effects of DEM outliers on DEM matching and automated removal of outliers. The major contribution of this paper is that we validate DEM matching, theoretically and experimentally, for estimating position and attitude biases and for establishing rigorous sensor models for pushbroom images.