In this study, a method is proposed to enhance the low-illumination remote sensing images based on a conditional generation adversarial network, so as to improve their visibility. First, low-illumination images were synthesized as training samples based on the clear images with normal illumination to solve the problem of insufficient sample data. Then, the original low-illumination remote sensing images were converted from the RGB color space to the HSI color space. Subsequently, channel splitting was performed to effectively separate the H, S, and I components, keeping the hue component H unchanged. Further, the conditional generation adversarial network and the improved logarithmic transformation method were used for processing the luminance component I and the saturation component S, respectively. Finally, channel merging was performed to implement the conversion of processed images from HSI color space to the RGB color space. The phenomenon of highly imbalanced sample proportion can be solved by adding focus loss function to the loss function. The experimental results show that the proposed method effectively improves the brightness and contrast of the low-illumination remote sensing images. Furthermore, this study provides novel concepts with respect to the development of low-illumination remote sensing image enhancement methods.