Advances in DSM generation and higher level information extraction from high resolution optical stereo satellite data

The automatic generation of digital surface models (DSM) of urban areas from high and very high resolution (VHR) stereo data from satellites is still a research issue. Image matching algorithms from computer vision have been introduced and adopted to satellite imagery in recent years. These algorithms do not work using local optimisation like area based matching but try to optimize a global cost function. Analysis shows that matching approaches based on epipolar images like semi-global matching (SGM) and new methods using total generalized variation (TGV) yield the best results. Especially satellites like Worldview-2, GeoEye-1 and Pleiades exhibit very high spatial resolution and geometric quality and can therefore be used to generate DSMs with good properties. If several stereo images from one orbit are available, a combination of DSMs generated by different stereo pairs leads to even better results. Using these DSMs, which already show urban area features in high detail, further higher level information can be extracted and new products can be generated.