A Channel-Aware Adaptive Semantic Communication Method for LEO Satellite Remote Image Transmission

Satellite-ground semantic communication is an important component of the forthcoming 6G era. Due to the strict bandwidth limitations of low earth orbit (LEO) satellites, efficient transmission of massive satellite remote sensing images is difficult. To this end, we propose a channel-aware adaptive semantic communication method for LEO satellite remote image transmission. The proposed method includes a feature extraction module, an important feature enhancement module, a rate adaptive module, and the corresponding decoding modules. The feature extraction module can extract global context information via lightweight mamba. The important feature enhancement module can enhance useful features via the involution layer and the signal to noise radio (SNR) adaptive block according to channel state SNRs. The rate adaptive module can further adaptively adjust the size of transmission features according to the transmission rate via the rate adaptive block and the rate mask block. The extensive experimental results on the WHU-RS19 dataset demonstrate that our method obtains higher peak signal to noise radio (PSNR), multi-scale structural similarity index measure (MS-SSIM) and learned perceptual image patch similarity (LPIPS) than state-of-the-art methods under low SNR and limited bandwidth conditions.