Requirements for higher resolution remote sensing images lead to rapid increase of data amount in space communications. However, since satellite communications capacity is suffering from great pressure, seeking for more effective compression scheme is supposed to solve existing conflict between tremendous data and limited bandwidth. For this reason, this paper proposes a prior-information-based remote sensing image compression scheme. We firstly utilize prior information contained in historical remote sensing images for incremental image extraction, which is assumed to have removed redundant information possessed both on the satellite and ground. Moreover, Bayesian dictionary serves to sparsely represent the incremental image, generating finite number of representation coefficients in place of numerous pixels. Finally, quantization and encoding schemes are further designed for efficient data transmission. Experimental results show that the proposed scheme is competitive to existing general image compression schemes.