Hazes reduce the quality of remote sensing images while limiting the performance of back-end visual applications. Hence, this study proposed a multi-scale residual dehazing network based on dual attention. First, an atmospheric scattering model was constructed to combine the atmospheric light value and transmissivity to derive the atmospheric power of light. Second, an end-to-end deep learning model was used to clarify remote sensing images with hazes. The dehazing network consists of a shallow feature extraction module, a deep data extraction module, a dual mapping network, and a parallel convolution reconstruction module. Finally, the proposed dehazing network was compared with CARL-net, DFAD-net, SRBFP-net, and AMGP-net through subjective and objective comparison experiments. The results indicate that the proposed dehazing network obtained a visual state close to the original haze-free scene, exhibiting high contrast, bright chroma, corresponding saturation, and clear transmission map details. Moreover, it effectively removed image noise while maintaining the edge of the foreground part. Compared to the above four networks, the proposed dehazing network achieved superior peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM), higher algorithm processing efficiency, and stable algorithm processing time with the increase of image resolution.