## 1 Introduction  
Ground resolution, ground coverage width, and payload weight are three key indicators of spaceborne remote sensing. Ground resolution is the sharpness of the image, which determines the spatial resolution of remote sensing satellites. The ground coverage width is the vertical orbit coverage width under the condition of single orbit, which determines the time resolution of remote sensing satellites. The payload weight determines the capability of the satellite platform. These three indicators are actually mutually restrictive, and to guarantee two indicators, the other must be sacrificed [1]. From the perspective of spaceborne remote sensing, improving the cost-effectiveness of Earth observation missions on the basis of satisfying information needs is the development goal of space remote sensing. The improvement of various indicators of small satellites makes small satellites gradually replace the status of large satellites in some missions. According to the simplified definition proposed by The International Academy of Astronautics (IAA) more than 30 years ago, a satellite weighing less than 1000 kg is a small satellite, as shown in Figure 1[2]. Small satellite missions are primarily accomplished in two ways. One approach is to focus on a single mission, using multiple small satellites for constellation networking. Another method is to make full use of the developing technology to design new payloads with outstanding characteristics to complete high-performance Earth observation missions. However, in some Earth observation missions, a certain amount of ground resolution is selectively sacrificed in order to ensure the coverage width. Some common Earth observation missions and resolutions in practical missions are shown in Figure 2[3]. Therefore, in order to be competent for more tasks, how to greatly increase the ground coverage width under the premise of ensuring high resolution is a particularly important issue. There are currently three mainstream wide-swath imaging methods: multi-sensor inner field of view (FOV) stitching, multi-camera outer FOV stitching, and agile imaging based on satellite maneuverability. Figure 1: The satellite differentiation standard based on mass. Figure 2: Common Earth observation missions and their resolution in practical missions. Multi-sensor inner FOV stitching is the most widely used imaging method, especially in push-broom imaging modes using linear CCDs. In order to enlarge the swath width, splicing of CCDs is usually required. The traditional method of multi-sensor inner FOV stitching is direct stitching, and the detector is used for FOV stitching. Due to the limitation of physical factors such as the structure of the detector and the outer packaging of the device, it is difficult to arrange the CCDs directly in a line, so the multi-sensor inner FOV stitching requires a stitching algorithm to obtain continuous and seamless remote sensing images [4; 5]. For example, the QuickBird satellite launched by the United States in 2001 was the first commercial satellite in the world to offer sub-meter resolution products. The camera is composed of 6 CCDs staggered and stitched, and can obtain images with a resolution of 0.61 m and a wide swath of 16.5 km [6]. The Wide Field of View Camera (WFV) carried by the Gaofen-6 (GF-6) satellite launched by China in 2018 can cover 800 km on a single track and has a resolution of 16 m, which is the highest level of observation width among similar satellites in the world. WFV is stitched by eight pieces of complementary metal oxide semiconductors (CMOSs) and uses an off-axis four-mirror optical system to obtain a large FOV of 65.64\({}^{\circ}\)[7]. The new method of multi-sensor inner FOV stitching is indirect stitching, which uses reflection and transmission to split the FOV into multiple CCDs. For example, the ZY-3 satellite uses a half-mirror to stitch three CCDs together, and a product with a resolution of 2-5 m and a width of 50 km can be obtained [8].