Remote sensing using satellite images plays a crucial role in gathering data about the Earth's surface, but haze degradation at low bandwidth can significantly compromise image clarity and utility. This paper introduces a hybrid dehazing method designed specifically for low bandwidth satellite images using Generative Adversarial Networks (GANs). The approach enhances the visual quality of hazy satellite imagery by integrating deep learning and advanced image processing techniques to counteract atmospheric effects while preserving essential image details necessary for remote sensing applications. Through experimental analysis, the proposed method exhibits its ability to produce clear, high-fidelity satellite images even under dense haze conditions. Compared with state-of-the-art methods such as DCP, Dehaze-Net, U-Net, and AIDED-Net, the hybrid approach shows a reduction in root mean square error of approximately 13% to 15% across different haze intensities and an average improvement of 34% in the structural similarity index measure. For dense hazy images, the method achieves a minimal AMSE value of 4.2, outperforming other techniques that fall in the range of 4.4 to 5.7. These results confirm the proposed method as a promising solution for enhancing satellite image quality across varying environmental conditions.