## 1 Introduction
The potential of VHR optical sensors for 3D city model generation has been addressed in [15; 16; 17], showing promising results for automatic building extraction when compared to a LiDAR elevation model, although highlighting some difficulties in the case of small individual house reconstruction. A quantitative and qualitative evaluation of 3D building models from different data sources was presented in [18], where a DSM at 1 m resolution derived from a GeoEye-1 stereo-pair, a DSM from an aerial block at 50 cm GSD, and a LiDAR-based DSM at 1 m resolution were used. Their results show that the percentage of correctly reconstructed models is very similar for airborne and LiDAR data (59% and 67%, respectively), while for GeoEye data it is lower (only 41%). The real dimensions of the 17 buildings surveyed were used as ground truth-reference for the 3D building model's quality assessment, obtaining a mean value for the residual heights of 1.94 m for the photogrammetric DSM. In [19] the authors analyze and validate the potential of high-resolution DSMs produced from stereo and tri-stereo Pleiades-1B satellite imagery acquired over the Athens Metropolitan Area. From their tests, the tri-stereo model shows the best performance in height accuracy, with an RMSE of 1.17 m when compared with elevations measured by a differential global positioning system. The advantages of using tri-stereo instead of stereo image pairs are described by [PERSON] et al. [20], where the nadir image increases the DSM completeness, reducing the occlusions usually caused by larger convergence angles on the ground. Additionally, they investigate in detail the influence of the acquisition geometry (viewing and incidence angles) of VHR imagery on DSM accuracy. The cited literature concentrates on the accuracy assessments either on open areas or on (large) buildings within city areas, but not on smaller objects like cars. The standard method of DSM generation from stereo-pairs or triples is dense image matching using global or semi-global optimization. Because of the smoothness constraint of dense image matching [21], the heights of small individual objects may not be reconstructed. Hence, the corresponding 3D points will not have higher elevations compared to their surroundings. Based on this hypothesis, we investigated the capability of dense image matching when evaluating the height of small individual, i.e., detached, objects. While previous studies addressed the general 3D capabilities of VHR sensors, our investigation concentrates on small, isolated object detectability by height estimation. To the best of our knowledge, our study is the first to analyze and quantify the capability of the tri-stereo Pleiades and WorldView-3 reconstructed DSMs for single object detection by height estimation, with focus on vehicles, buildings, and trees. The object's height compared with its surrounding terrain (in the following simply referred to as height) was investigated with respect to the following parameters: building length and width, vehicle length and width, and tree crown diameter. We investigate DSMs from different sensors with very small GSD, Pleiades and WorldView-3, but the focus is not a comparison of the two satellite systems. Specifically, our research investigation's purpose is to answer the following questions: (1) which geometric signature and minimum size must individual objects have to be detected in the DSM-based on their reconstructed heights; and (2) what are the influences of different acquisition times, geometries, and GSDs on dense image matching quality for single objects. In the following, we first describe the tri-stereo satellite imagery used together with the study site (Section 2), followed by image block orientation (in Section 3.1) using Rational Polynomial Coefficients (RPCs) and a set of GCPs. Once the orientation is completed, the 3D reconstruction follows. The 3D coordinates of the specific points corresponding to buildings, vehicles and trees are monoscopically measured in all three images: forward, nadir, and backward.