High Quality Compression and Transmission of Remote Sensing Images Based on Semantic Communication

Abstract

Remote sensing imagery plays a crucial role in areas such as environmental monitoring and urban planning. However, due to fragile communication links, limited bandwidth and harsh wireless environments, transmitting data from remote locations to ground applications faces the dilemma of high bit-error rates, which have a poor impact on downstream missions. Semantic communication is a feasible solution that transmits only the semantic features of the raw data extracted using neural networks. Although effective, existing semantic communication methods cannot cope with high compression rate requirements and complex communication environments. Therefore, in this paper, an effective image compression and transmission framework ASE-JSCC is proposed. To minimize the transmitted data, we design a semantic extraction module and an important feature selection module to efficiently extract, select, and compress critical semantic features required for downstream tasks. To improve the communication robustness of the model in complex environments affected by variable channels, we optimize the source-channel joint coding technique by randomly adding noise with different types and sizes. Finally, we deploy ASE-JSCC to the scene classification task of remote sensing images and conduct extensive experiments on four real datasets, achieving classification accuracy of 84.29%--88.62% under 384 times compression ratio, verifying the excellent performance of the proposed framework.