A research and strategy of space super-resolution imaging system based on Sandroid CubeSat
Hailu Wang, Junsuo Zhao, Fengge Wu · 2016
Low-resolution has always been an issue of space satellite imaging system, which results in hard positioning and identifying the observation target. Traditional method to enhance the spatial satellite imaging resolution is strengthening the camera hardware such as sensor, lens and the like, which produces an expensive cost (up to hundreds of millions of dollars) and brings about a long development cycle. Proposing an efficient solution of space super-resolution (SR) imaging plays a significant role for the academia, industries and space agencies. This paper proposes a spatial super-resolution imaging strategy applying the SR algorithms to Sandroid CubeSat platform based on its computing power which realizes the goal of obtaining high-quality images with lower possible cost. First of all, the progress of satellite space imaging and SR technology is surveyed respectively. Subsequently, the principles of representative and state-of-the-art SR approaches are explained in details, then they are applied to the satellite images, afterwards advantages and defects of different SR algorithms in terms of visual results and objective measures are discussed systematically. Following the experiment, the feasibility of SR algorithms for satellite images is analyzed. Ultimately, according to the comprehensive analysis, the paper proposes a spatial super-resolution imaging strategy. The outstanding contribution of this paper is to integrate Sandroid CubeSat with powerful computation ability and the existing SR technology to upgrade the resolution of space imaging, which can achieve the target of using a smaller, lighter, cheaper camera to obtain HR image.