# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 1 Introduction  
The continuous development of remote sensing technology has facilitated Earth 3D surface reconstruction, which is widely used in disaster prevention and mitigation, emergency preparation, urban planning, etc. This reconstruction can be achieved by applying geometric analysis after the acquisition of satellite photogrammetric images with proper intersection angles between them. Compared to UAV aerial imagery, satellite imagery is more cost-effective in covering a wider geographic area, and it can capture Earth observation data in a shorter period, thus acquiring a 3D reconstructed view of any target area on a global scale [1]. In addition, the high spatial resolution of satellite images makes it possible to reconstruct high-quality 3D scene information from these images [2, 3, 4], for which stereo matching algorithms need to be applied to generate a dense 3D point cloud and DSM. To recover the detailed 3D information of scenes such as cities, streetscapes, and buildings, at least two high-resolution images are needed to provide accurate stereo matching. For example, [PERSON] et al. proposed a double propagation stereo matching (DPSM) method forurban 3D reconstruction from stereo-rectified satellite images, which leverages superpixel-based geometric models and three similarity metrics to optimize disparity estimation, while demonstrating superior performance in preserving depth discontinuities and handling occlusions [5]. [PERSON] et al. introduced a generalized stereo matching method that addresses challenges such as the radiation difference and small ground feature difference in satellite images [6]. Their approach utilizes hierarchical graph structure consistency and iterative optimization to improve matching accuracy in urban 3D scene reconstruction. Similarly, [PERSON] et al. proposed a comprehensive pipeline for image-based 3D reconstruction of urban scenarios, employing PatchMatch Multi-View Stereo (MVS) techniques [7]. Furthermore, [PERSON] and [PERSON] developed ResDepth, a deep residual prior for 3D reconstruction from high-resolution satellite images [8]. This convolutional neural network refines initial stereo digital surface models by conditioning the refinement on the images, effectively improving the quality of 3D reconstructions in urban settings. These studies underscore the ongoing efforts to enhance 3D reconstruction methodologies by integrating advanced stereo matching techniques and machine learning approaches. Currently, satellite observation of the Earth mainly relies on the push-broom mode to obtain remote sensing images, but this method is prone to errors in stereo correction [9]. The quality of 3D content generation using satellite image pairs can be affected by many factors and is still questionable and unpredictable. For example, most of the literature focuses on the effect of the intersection angles (base-height ratios) on the dense matching results primarily for in-track satellite stereo pairs [10; 11]. Instead, [PERSON] et al. pointed out that the intersection angle is insufficient for stereo pair selection and emphasized the importance of sun-angle difference in determining stereo pair quality [12]. Moreover, [PERSON] et al. analyzed the correlation of the angle between the views, maximum incidence angle, and time difference between the acquisitions, with the quality of DSM [13]. The authors in [14] suggested that the stereo convergence angle should have a value between 5\({}^{\circ}\) and 15\({}^{\circ}\), and the difference in sun angles between the two images should be below 25-30\({}^{\circ}\) for satisfactory stereo constructions. Though these conclusions can be useful in guiding multi-view satellite image reconstruction to improve the quality of DSM, such work is carried out in an acquisition-selection manner, i.e., the satellite images are first acquired and then selected to form the stereo pairs.