Rapid image product generation from high-resolution satellite imagery for disaster management

The Indian-ocean tsunami occurring (in the morning of 26 December 2004 caused the most devastating disaster in the known history of Thailand. Remotely-sensed data, particularly high-resolution satellite imagery such as 1m Ikonos images, played a crucial role as an up-to-date post-event information source for a variety of user groups including rescue teams, relief and aid workers, and policy makers. However, ready-to-use low-level image products shipped by the provider can contain absolute positioning errors ranging from 5 - 30 m. Mismatches can therefore occur when georectified imagery is overlaid with vector data such as transportation networks or landmarks and this can complicate further analysis, both visually and analytically. More metrically precise image products can be generated through photogrammetric techniques, but systematic errors in the provided rational polynomial coefficients (RPCs), which describe the relationship between image and ground coordinates, can still cause computed absolute ground coordinates to be in error by several pixels. The bias-compensation technique described in this paper can be used to correct RPC values. Test results on stereo-pairs of Ikonos images have shown that the resulting horizontal error of bias-corrected imagery is reduced to just above the 1m level, making the image comparable in terms of metric accuracy to a 1:5,000 scale map. This 1m level accuracy can be achieved with only one ground control point, the coordinates of which are measured by the real- time kinematic (RTK) GPS technique. Furthermore, field control point measurements can be totally avoided if large-scale maps of the desired areas exist and well-defined points can be found to be present in both the map and image. The presented bias-compensation technique has clearly demonstrated its suitability as a rapid geopositioning correction tool for the generation of precise satellite image products for disaster management.