Towards Automated Dem Generation from High Resolution Stereo Satellite Images

High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). In this paper, a system for highly automated DSM and orthoimage generation based on CARTOSAT-1 imagery is presented. The proposed system processes photometrically corrected level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. Ground control points are used to estimate affine RPC correction. Accurate GCP are not always available, especially for remote areas and large scale reconstruction. In this paper, GCP are automatically derived from lower resolution reference images (Landsat ETM+ Geocover and SRTM DSM). It is worthwhile to note that SRTM has a much higher lateral accuracy than the Landsat ETM+ mosaic, which limits the accuracy of both DSM and orthorectified images. Thus, affine RPC correction parameters are estimated by aligning a preliminary DSM to the SRTM DSM, resulting in significantly improved geolocation of both DSM and orthoimages. Robust stereo matching and outlier removal techniques and prior information such as cloud masks are used during this process. DSM with a grid spacing of 10 m are generated for 9 CARTOSAT-1 scenes in Catalonia. Checks against independent ground truth indicate a lateral error of 3-4 meters and a height accuracy of 4-5 meters. Independently processed scenes align at subpixel level and are well suited for mosaicing.