###### Abstract  
Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds. optical satellite imagery; 3D reconstruction; deep learning; generative adversarial network (GAN); RPC model +
Footnote †: journal: Applied Physics and Computational Mathematics, Beijing 100094, China  
pinhole model, satellite imagery is imaged by a pushbroom camera, and the general Rational Polynomial Camera (RPC) model is often used to describe the imaging process [5]. However, pushbroom camera imaging will lead the epipolar line of the satellite stereo pair to the epipolar curve, resulting in an epipolar constraint error [6]. At present, a popular method of stereo rectification of satellite image pairs is affine model approximation [7], that is, the affine matrix is generated according to the matching point pairs of the stereo pairs, and then, the affine transformation is performed on the satellite image stereo pair. The affine model approximation method is simple and possesses high rectification accuracy. Stereo matching is an important step in the 3D reconstruction of satellite images. The most commonly used stereo-matching method is using the semi-global matching (SGM) algorithm [8; 9; 10; 11]. The SGM algorithm approximates the two-dimensional matching cost computation by means of one-dimensional path cost aggregation. However, the SGM algorithm is prone to generating error streaks. Therefore, some researchers propose the more global matching (MGM) algorithm [12], which extends the one-dimensional path cost aggregation to the two-dimensional quadrant aggregation. Even though this method improves the matching accuracy, it is time consuming. Besides the SGM and MGM algorithm, some researchers use the patch match method in the field of computer vision to implement stereo matching [13; 14]. The patch match method has the problem of fixed disparity in the window, which will lead to the distortion of the edges in images during matching. With the popularity of deep learning, some researchers propose an intelligent stereo-matching method, such as using a convolutional neural network (CNN) to calculate the matching cost [15] and using smart aggregation strategy in the SGM algorithm [16]. Although these methods can obtain good matching results, they require a large number of satellite images for training. The final step of the satellite images 3D reconstruction is altitude estimation, which uses triangulation to calculate 3D coordinates according to the disparity map generated by stereo matching. The triangulation of satellite imagery requires the RPC model [17]. Therefore, the accuracy of the RPC model is particularly important. [PERSON] et al. proposed an RPC model refinement method based on bundle adjustment [18], which can improve the accuracy of satellite image 3D reconstruction. From previous studies, it can be seen that the quality of satellite images has a great impact on 3D reconstruction [4; 19]. Therefore, a GAN image enhancement-based 3D reconstruction method is proposed in this paper.