## 4 Experimental Results and Discussion
### Performance Analysis of the Proposed Method  
We select eight sites from IARPA MVS3 DM dataset. There are 400 images from the eight sites, and when training the generator network, we selected 289 images as the training set, 29 images as the validation set, and 82 images as the test set. Since the dataset contains multi-date satellite images, we use an experience-based satellite stereo pair selection strategy [4]. The input satellite images are shown in Figure 5. Site 1 is a mixed area of low-height buildings, medium-height buildings and roads, whose altitude range is from 18 m to 35 m. Site 2, Site 5, and Site 6 are low-height building areas with an altitude range of 18 m-35 m. Site 3 is a mixed area of medium-height buildings and low-height buildings with an altitude range of 18 m-42 m, whose altitude of medium-height buildings is a little bit higher than that of Site 1. Site 4 and Site 7 are areas that have high buildings with an altitude more than 80 m. Site 8 is a park area with an altitude range of 15 m-55 m, and there are some water surfaces in this area. These 8 sites contain a variety of heights and different types of buildings, which can fully verify the performance of the proposed method. We compare our method with the S2P method [7] and the BA method [18]. The S2P method is one of the most accurate 3D reconstruction methods at present, which achieves high-precision and rapid reconstruction through ingenious geometric rectification of satellite images. The BA method is a 3D reconstruction method of satellite images that optimizes the RPC parameters by the bundle adjustment algorithm, and this method can generate good reconstruction results when there are multi-view satellite images. The ground truth DSMs and the reconstructed DSMs are shown in Figure 6. For comparison and visualization, we represent DSMs as RGB images, using red for high-altitude areas and blue for low-altitude areas. The elevation range for each site is indicated on the rightmost part of Figure 6. The details of the reconstructed results of Site 1 are shown in Figure 7.  
For Site 1, it can be seen from Figures 6 and 7 that the S2P method generates poor reconstruction results in the building area of the upper-left corner, and the BA method has a low reconstruction elevation in the 35 m building area (this area is orange instead of red), and the method proposed in this paper has a better reconstruction result in this area. As for Site 2 and Site 3, the reconstructed results of the S2P method in the lower-left and middle areas are still obviously distorted, while the reconstruction results of the BA method and the proposed method are blurred at the edge of the high-rise buildings and the left edge of the DSM. As can be seen from the fourth row of Figure 6, the reconstructed DSMs of Site 4 are poor for all three methods, due to the fact that Site 4 contains too many vegetated areas. For Site 5 and Site 6, the visual quality of the reconstruction results of the S2P method and the proposed method is slightly better than that of the BA method, which may be due to the fact that the BA method does not obtain the optimal value when bundle adjustment optimizes the RPC parameters. The results of Site 7 and Site 8 reconstructed by the three methods has poor visual quality due to the fact that these two sites contain multiple forms of buildings and the height difference between buildings is pretty large. Due to the large architectural difference, the 3D reconstruction methods will reduce the number of matching points for stereo matching, resulting in inaccurate altitude estimation. Figure 5: The input satellite images of the 8 selected sites from the IARPA MVS3 DM dataset. Figure 6: _Cont_. We performed quality evaluation experiments on the reconstructed DSMs shown in Figure 6, the experimental results of which are listed in Table 1. The metrics marked in bold and green are those with better quality evaluation.