Design and accuracy analysis of remote sensing imaging system based on spatio-temporal encoded modulation

In low-light conditions, high resolution remote sensing cameras usually require a long exposure time, during this process, the relative motion between satellites and ground objects leads to a degradation in imaging quality. Addressing this issue, this paper proposes a low-light staring remote sensing imaging technology based on compressive imaging theory. This technique slices the long exposure process into many short temporal frames, using spatial-variant mask to code each frame. these modulated time frames are collected into compressive measurements that contain spatio-temporal information. Then, a specific algorithm is used to reconstruct short time frames by compressed measurements and masks, finally solve the motion blur problem caused by long exposure, Utilizing this technology, this paper simulated experiments and analyses of factors such as mask form, target motion speed, satellite platform vibration, and encoding time error. The main experimental conclusions are as follows:1. Random binary mask exhibits better reconstruction performance compared to random uniform mask when the number of frames is less than 10; 2.For moving targets, under the pupil radiance is 1×10^-2W/(m^-2×sr), clear imaging of cars traveling at 80 km/h can be achieved (PSNR exceeding 35 dB); 3.When the maximum platform vibration amplitude is approximately 1/4 of a pixel, The PSNR can reach 40dB, meeting the requirements of high-resolution remote sensing imaging; 4. Introducing encoding time error causes PSNR degradation of about 1 dB, with minimal impact on imaging quality. These research results contribute a certain theoretical basis and engineering reference for the study of weak-light remote sensing imaging.