Despite the rapid development of satellite sensors, acquiring high-resolution true-color images remains challenging. The reliance on paired panchromatic and multispectral images, as required by traditional techniques such as pansharpening, presents a significant challenge in scenarios where such data are not available. In this paper, a GAN-based PAN-to-RGB model is proposed to establish a novel framework for high-resolution, high-fidelity true-color images generation from remote sensing data. Symmetric luminance-color decoders are leveraged to learn the mapping between luminance, color, and spatial multi-scale features, effectively overcoming the color desaturation, inaccuracies, and distortion common in existing algorithms. By combining CNNs for local feature modeling and transformers for global feature modeling, high-resolution, high-fidelity RGB images were generated in CIELAB space. We conducted experimental validation on Gaofen-7 satellite data, and the results demonstrated that the proposed algorithm effectively mitigates the issues of desaturated, inaccuracy, and distorted colors, achieving significant improvements in key metrics such as FID, CF, and <em>ΔCF</em>.