Efficient LEO Satellite Data Management Using No-Reference Image Quality Assessment

Low Earth Orbit (LEO) satellite communications for Internet-of-Things (IoT) services provide a cost-effective means to improve existing networks. However, challenges related to data, such as storage capacity, bandwidth consumption and image transfer speed, necessitate the development of real-time data management solutions for LEO satellite communications. In this paper, we introduce a comprehensive no-reference deep image quality assessment (NR-DIQA) framework designed to effectively manage LEO satellite images for Earth observation (EO) by identifying and filtering distorted and anomalous images before transmission. The proposed approach employs a labeling methodology for remote sensing images (RSI) with a deep learning (DL) model to compute a score that assesses image quality (IQ) and determines the set of high-quality images to be transmitted to the ground station. We show that the proposed approach overcomes a baseline model and show that extensive data savings could be achieved for EO applications.