FULLY AUTOMATED GENERATION OF ACCURATE DIGITAL SURFACE MODELS WITH SUB-METER RESOLUTION FROM SATELLITE IMAGERY
Modern pixel-wise image matching algorithms like Semi-Global Matching (SGM) are able to compute high resolution digital surface models from airborne and spaceborne stereo imagery. Although image matching itself can be performed automatically, there are prerequisites, like high geometric accuracy, which are essential for ensuring the high quality of resulting surface models. Especially for line cameras, these prerequisites currently require laborious manual interaction using standard tools, which is a growing problem due to continually increasing demand for such surface models. The tedious work includes partly or fully manual selection of tie- and/or ground control points for ensuring the required accuracy of the relative orientation of images for stereo matching. It also includes masking of large water areas that seriously reduce the quality of the results. Furthermore, a good estimate of the depth range is required, since accurate estimates can seriously reduce the processing time for stereo matching. In this paper an approach is presented that allows performing all these steps fully automated. It includes very robust and precise tie point selection, enabling the accurate calculation of the images’ relative orientation via bundle adjustment. It is also shown how water masking and elevation range estimation can be performed automatically on the base of freely available SRTM data. Extensive tests with a large number of different satellite images from QuickBird and WorldView are presented as proof of the robustness and reliability of the proposed method.