Comparative Evaluation of Image Compression Algorithms for Leo Nanosatellite Remote Sensing

In the contemporary digital era, where transmission and imaging technologies are crucial, satellite-based remote sensing has emerged as a key player. Specifically, nanosatellites in low Earth orbit (LEO) are essential for such applications, although they face significant challenges related to bandwidth limitations. Due to their substantial data size, these limitations restrict the direct transmission of high-resolution images from satellites to ground stations. Addressing critical issues, this paper presents a methodology for the selection of the best methods for image compression as a potential solution with the comparison between existing technologies to elucidate its advantages in a realistic scenario. The goal is to facilitate the efficient transmission of high-resolution imagery within bandwidth constraints, thereby enhancing the operational efficacy of nanosatellite applications in remote sensing.