## 1 Introduction  
The Earth observation technology has developed rapidly in the past few years, which has led to a large increase in high-resolution satellite images. The massive images provide powerful data support for Digital Surface Model (DSM) generation. Satellite image DSM generation technology can provide abundant spatial information and reliable data support for urban planning, resource management, environmental monitoring, disaster prevention, and other fields [1]. Therefore, the development of DSM generation technology for satellite images has important practical significance and application prospects. According to the input data type, the DSM generation from satellite images can be divided into generation from image pairs and from multi-date images [2,3]. The DSM generation from image pairs requires stereo rectification [4], dense matching [5], and triangulation [6] to generate the 3D point cloud. The Satellite Stereo Pipeline (S2P) method [7] proposed by [PERSON] et al. is an outstanding representative of the DSM generation of satellite image pairs, which corrects the relative pointing error of the image pairs and realizes automatic DSM generation [8,9]. As far as we know, the generation speed of the S2P method is pretty fast, but the accuracy of the generated DSM may be improved. Later, [PERSON] proposed the RPC (Rational Polynomial Coefficient) Stereo Processor (RSP) [10] for DSM generation, and [PERSON] et al. further combine the ground video frames with the satellite images for cross-view registration in the follow-up research to enrich the details of the generated DSM [11]. The later method can reduce holes in generation, but its pipeline is complicated. Recently, with the study of deep learning emerging, some researchers used deep learning algorithms to process the dense matching in the satellite images DSM generation pipeline and obtained some good generation results [12; 13; 14; 15]. However, the deep-learning-based methods are uninterpretable and have weak generalization ability, moreover, they require a large amount of data and computing resources to train the model, so it is difficult to use them in some important tasks (such as emergency cartography). In recent years, the researchers have proposed some new multi-view pipelines of satellite imagery. For example, [PERSON] et al. propose a stereo pipeline to deal with different landscapes and image defects [16]. [PERSON] et al. generate a more accurate satellite stereo parallax map based on image registration and correction [17]. Some researchers propose DSM generation methods based on small satellite data [18] and reconstruction methods based on images collected by different satellites [19]. As roughly suggested by [20], the DSM generation from multi-date satellite images can be divided into two types: the multi-view stereo method based on dense stereo matching, and the true multi-view method that simultaneously triangulates all images. The multi-view stereo method needs an image pair selection strategy, which selects two images with high correspondence to form a stereo image pair from the multi-date satellite images. Then the selected satellite image pair is used to generate multiple 3D point clouds. The multi-view stereo method finally aligns and fuses the 3D point clouds and then generates the final DSM. In this sense, the multi-view stereo method can be regarded as an extension of the DSM generation from satellite image pairs. For multi-date satellite images, the researchers of S2P proposed an image pair selection strategy based on the intersection angle and acquisition date to realize the automatic DSM generation [20]. [PERSON] trained and analyzed the meta-data of intersection angle, sun azimuth angle, sun elevation angle, etc. with the Support Vector Machine (SVM) model [21] and gave an image pair selection strategy according to the SVM analysis results. This method is very comprehensive but requires a large amount of training data. Later, [PERSON] et al.