Deep neural networks have been successfully used in image processing and have benefitted image compression technology. On this basis, deeper and more extensive use of compression algorithms in remote sensing applications was investigated in our study. As a general residual block does not effectively utilize the connections between channels and spatial regions in the reconstruction process, an attention mechanism codec framework based on the channel-spatial attention residual block was introduced. Consequently, the network can account for the interdependence between channels and enhance specific spatial areas. Simultaneously, the feature importance map makes the network focus on valuable information, especially for small objects and remote sensing images with many tiny details, which is crucial to describe their contours. Introducing the attention mechanism commonly used in target detection can effectively recover the missing detailed information for remote sensing compressed images. Moreover, to utilize the underlying information of the various scene categories and characterize the feature differences between scenes in remote sensing images, an adaptive scene-aware module was introduced to enhance the feature representation in remote sensing images of different scenes. The experimental results show that the proposed method has superior subjective and objective effects in remote sensing image compression tasks.