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 <xref ref-type="disp-formula" rid="deqn1-deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> normalized and passed through a high-performance cloud filtering via the Cloud-SLR model, <xref ref-type="disp-formula" rid="deqn1-deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> 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 <xref ref-type="disp-formula" rid="deqn3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(3)</xref> 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.