## 1 Introduction  
The new generation of very high-resolution (VHR) commercial satellites with ground sample distance (GSD) lower than 1 m started in September 1999 with the launching of the IKONOS-2 satellite. Nowadays, an increasing number of VHR optical satellites with stereo (or triplet) image capabilities are commercially available. Among them, we can highlight QuickBird-2 (from 2001 to 2015, 0.61 m GSD), EROS B1 (from 2006, 0.70 m GSD), Resurs-DK1 (from 2006 to 2016, 0.70 m GSD), KOMPSAT-2/Arriang-2 (from 2006, 1 m GSD), IRS Cartosat-2 (from 2007, 0.80 m GSD), WorldView-1 (from 2007, 0.50 m GSD), GeoEye-1 (from 2008 to 2018, 0.41 m GSD), WorldView-2 (from 2009, 0.46 m GSD), Pleiades-HR 1A (from 2011, 0.7 m GSD), Kompsat-3 (from 2012, 0.70 m GSD), Pleiades-HR 1B (from 2012, 0.7 m GSD), Skysat (from 2013, 0.90 m GSD), Kazeesat-1 (from 2014, 1 m GSD), Deimos-2 (from 2014, 1 m GSD), WorldView-3 (from 2014, 0.31 m GSD), Kompsat-3A (from 2015, 0.55 m GSD), TripleSat (from 2015, 1 m GSD), Teleos-1 (from 2015, 1 m GSD), WorldView-4 (from 2016 to 2019, 0.25 m GSD), Vision-1 (from 2018, 0.9 m GSD) and SuperView1A/1B (from 2018, 0.5 m GSD). Most of these VHR sensors have already been studied over the last 20 years [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11], with orthoimages and digital surface models (DSM) being the flagship georeferenced products derived from them. Both products need a sensor orientation phase (also known as the triangulation process) as a previous and crucial step. Regarding this sensor orientation phase, a zero-order (rational polynomial coefficient (RPC0)) polynomial transformation is usually the best option when using the triangulation method developed by [PERSON] and [PERSON] [12] based on 3D rational functions with vendor image support data (i.e., rational polynomial coefficients (RPC)) refined by a polynomial transformation in the image space. In this case, at least one ground control point (GCP) is necessary to compute the shift parameters through \(x\) and \(y\) axes. In fact, RPC0 was the best sensor model for IKONOS-2 [1], GeoEye-1 stereo pairs [4] and GeoEye-1 Geo and WorldView-2 Ortho Ready Standard [7]. However, there are a few VHR satellites (e.g., QuickBird-2) that need a more complex transformation to correct higher order distortions in the images [13]. Due to this, first-order, also called affine transformation (RPC1), and second-order polynomial transformation (RPC2) are also usually applied at the sensor orientation phase. This work is focused on Deimos-2 sensor, a VHR optical satellite operated by Deimos Imaging that launched on 19 June 2014. There are only a few published works dealing with pansharpening methods [14] or image classification [15] from Deimos-2 imagery. Regarding the geometric capabilities of the Deimos-2 level 1B (L1B) product, a technical report was published by the Joint Research Centre (JRC) in the frame of control with the remote sensing program of the European Common Agriculture Policy [16]. In this work, [PERSON] et al. [16] used ground points (GPs) with variable accuracies (from 0.05 m to 0.90 m measured as root mean square error in one direction, RMSE\({}_{\text{1D}}\)). Probably due to this poor GP support, they did not find differences between RPC0 and RPC1 sensor models when orthorectifying Deimos-2 images. It is noteworthy that they only tested both sensor models using three and four GCPs. With regard to DSM generation from VHR satellite stereo pairs, the semiglobal matching (SGM) algorithm [17] is often the most popular in routine production workflows where robustness and efficiency are required. This digital matching algorithm is considered very robust and is able to reduce large outliers in low or non-textured areas while preserving edges and sharp object boundaries [18], being the choice in most of the most recent published works [18; 11; 19; 20]. In [PERSON] et al. [19] and [PERSON] et al.