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1. Introduction
Although IKONOS imagery has been commercially available since early 2000, the use of this imagery for DSM generation has been restricted due to various reasons. Firstly, up until beginning of 2002 stereo imagery was available only for governments and national mapping organisations. Secondly, Space Imaging follows a closed policy and does not release the sensor model but provides rational polynomial coefficients instead. These are provided for stereo images and special versions of Geo imagery (so called OrthoKit) but at significantly higher prices. To these reasons, one should add the general problems of IKONOS imagery like cost, availability, lack of user control on imaging parameters and conditions etc. Since summer 2001, some commercial systems (Erdas Imagine, LHS Socet Set, Z/I Imaging Image Station, PCI Geomatics OrthoEngine) support to one or the other extent IKONOS imagery for import, stereo viewing and processing, orthoimage and DSM generation by using the rational polynomial coefficients (RPCs) provided by SI, estimating RPCs from ground control or using alternative (bundle adjustment, DLT) and partly proprietary mathematical models. While estimating RPCs from GCPs is both expensive (high number of GCPs required) and sub-optimal, use of bundle adjustment due to the undisclosed IKONOS sensor model is impossible. DLT, as shown in Fraser et al. (2002) could be used with GCPs, while proprietary sensor models (PCI) are of unknown quality, while raw IKONOS data which would be most appropriate for use with a strict sensor model are not available. As shown in Baltsavias et al. (2001) and Fraser et al. (2002), a very simple alternative geometric model, needing at least 3 GCPs, can achieve better positioning accuracy than the RPCs and at sub-meter level.
Investigations on DSM generation from high-resolution imagery include the following. Ridley et al. (1997) evaluated the potential of generating a national mapping database of maximum building heights for buildings at least 5 x 10 m in planimetry by using DSMs extracted by matching of 1m aerial imagery. They report that matching has a potential to provide the requested information if the DSM had a spacing of 1-3 m but with lower accuracy (1.5 – 3 m RMS) and completeness compared to manual measurements. Muller et al. (2001) use simulated 1m-resolution and IKONOS data for DSM generation and landuse determination to estimate effective aerodynamic roughness for air pollution modelling and determine position of trees close to buildings that may cause soil subsidence for insurance risk assessment. The most extensive results on DSM generation from IKONOS up to now have been reported by Toutin et al. (2001). Dial (2000) also reports the expected mapping accuracy of IKONOS. There are no published investigations on the other two operational high-resolution spaceborne systems (EROS-A1 and Quickbird-2). As far as the authors know, this is the first investigation deriving DSMs from multitemporal IKONOS images. This data are much more available than stereo data, they do not include epipolar resampling which possibly degrades geometric accuracy and are cheaper than stereo IKONOS. Naturally, multitemporal differences make matching more difficult, but as it is shown in Baltsavias and Stallmann (1992) it is possible with intelligent techniques to achieve good results even in case of large image differences.
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