Semantic Image Encoding and Communication for Earth Observation with LEO Satellites

The substantial volume of data generated by Earth observation (EO) satellites poses a significant challenge to the limited-rate satellite-to-ground links. This paper addresses the downlink communication problem of change detection in multi-spectral satellite images for EO purposes. The proposed method is based on a cohesive strategy capable of eliminating clouds and performing semantic encoding during image processing. This approach is a manifestation of semantic communication, as it encodes vital information for the target application, in the form of changed multi-spectral pixels (MPs) to minimize energy consumption. The proposed method is based on a three-stage end-to-end scoring mechanism, which quantifies the significance of each MP before determining its transmission. Specifically, the sensing image is (1) normalized and passed through a high-performance cloud filtering via the Cloud-SLR model, (2) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them, and forward to the ground station, and (3) reconstructed at ground gateway based on the reference image and received data. The numerical results show the effectiveness of the proposed framework in achieving energy savings of up to $58$\% while upholding the transmission of high-quality data for satellite-based EO applications.