Polynomial models as a tool for mapping High Resolution Satellite Imagery

The use of High Resolution Satellite Imagery (HRSI) has opened up opportunities for exploiting their high spatial resolution in mapping. Many remote sensing satellite sensors exist that can be effectively used for map production, including SPOT, IRS-1C/D, Ikonos, and recently QuickBird. Successful exploitation of the high accuracy potential of these systems depends on the accuracy of the mathematical models used for sensor geometry. However, in the absence of sensor calibration and satellite orbit information for most HRSI, practical approaches have to be adopted. A number of different sensor models are available in most software packages, among them polynomial models are especially popular due to their simplicity and reasonable accuracy. This paper presents a comparative analysis and evaluation of the use of different polynomial models, as opposed to satellite rigorous models, with different HRSI. Experiments were performed using different real data sets of IRS-ID, Ikonos, and QuickBird. Our analyses suggest that approximate mathematical models can be effectively used for rectification and 3D determination process after taking into consideration different factors that may affect the results such as the satellite inclination angles, differences in terrain elevations and sensor geometry.