## 3 Experimental Section
### Methods  
Digital terrain models are one of the most important sources of data used in geomatics and geoscientific analysis. The topographic information is required for many of the geometric, radiometric and atmospheric corrections of satellite data, both for optical and microwave instruments, and the production of orthoimages, more and more used in applications, also requires elevation data in the form of DSMs/DTMs [27]. Low and medium-resolution global DSMs are easily available free of charge, such as the GTOPO30 DSM [28] or the SRTM DSM (SRTM30 for USA and SRTM90 worldwide) [29], but high-resolution DSMs with suitable detail are still not available for much of the Earth's surface, and, when available, they do not always have enough accuracy for engineering and environmental applications [27]. Figure 8: Cartosat-1 Aft image collected over Salon de Provence (France) with superimposed the GCPs derived from the French DB ORTHO® database provided by the Principal-Investigator French Institut Geographique National. This study investigated terrain modeling using the high-resolution images captured by Cartosat-1 and commercial off-the-shelf software (RSI ENVI(r)), the influence of the GCPs in the generation process [30], the influence of the sampling interval and of the terrain's slope on the quality of the generated models [31, 32] and the influence of data processing on final results. Here we do not used procedures to filter vegetation and man-made infrastructures, so DSMs were generated. Moreover, the Cartosat-1 DSMs have not been edited for blunders (manually or automatically) as other authors reported in their investigations [50]. Even if in the practical production a check and editing for blunder should be performed, our goal was to test the performances of the DSMs generation using full automatic data processing (with the only exclusion of GCPs selection). The software used allowed to quickly create both relative DSMs, as well as absolute DSMs, from stereo images using the spacecraft's metadata. When creating absolute DSMs the terrain's elevations are referred to a geodetic datum through some GCPs, while for relative DSMs the terrain's elevation are referred to an arbitrary reference height (e.g., the lowest value in the scene) and no GCPs are needed. The generation of DSMs was carried out by means of automatic image matching [25]. The input Aft and Fore images were first transformed into a pair of quasi-epipolar images using RPCs and tie points. Quasi-epipolar images differ from true epipolar images because the differences between y coordinates of two corresponding image points (y-parallax) is small but not null, as occurs in true epipolar images [33]. However, as true epipolar images they show a displacement in the satellite flight direction (x-parallax) proportional to the terrain elevation, thus elevation data can be computed. Corresponding ground features were extracted using a coarse-to-fine hierarchical solution (image pyramids) with an image matching algorithm based on the Forstner operator [34]: matching features were found on the Aft and Fore images of the stereo pair, using the quasi-epipolar geometry to reduce the search space [35], and their displacements were transformed into elevation values. Finally, raster DSMs were generated by interpolating the elevation values computed and they were re-projected in a map coordinate system using few GCPs (only for absolute DSMs). One of the major factors determining the accuracy of the generated DSMs, as well as the processing time needed for their computation, is the number of image pyramids used during the image matching. When using few image pyramids the processing time is very short but the number of matched points may be very limited and the DSM's quality poor. When using many image pyramids the number and quality of matched points increase, thus the overall quality of the generated DSMs, but at the expenses of the computation time.