Based on the theory of compressed sensing， a method for efficient coding and reconstruction of optical remote sensing images is proposed to reduce the pressure of data acquisition and transmission faced by large area scan cameras. First， a multi-domain perception matrix is constructed combining the spatial and compressed sensing domains. Compression is realized while sampling， and multiple compressed domain information is obtained. Then， for multi-domain compressed information， a reconstruction method based on the Huber function is proposed to rapidly reconstruct high fidelity images. The results of the optical image coding and reconstruction techniques proposed in this paper have higher structural similarity（SSIM） and PSNR compared to JPEG compression methods. Using images of the Jilin-1 satellite， single target and scene infrared images yields a PSNR reaching 40 dB and SSIM exceeding 0.8. Based on these findings， an efficient system for coding and restoring optical images is designed. The proposed system can meet the need for rapid compression and high fidelity reconstruction on the satellite.