Pansharpening is a crucial task in remote sensing, enabling the generation of high-resolution multispectral images by fusing low-resolution multispectral data with high-resolution panchromatic images. This paper provides a comprehensive analysis of traditional and deep learning-based pansharpening methods. While state-of-the-art deep learning methods have significantly improved image quality, issues like spectral distortions persist. To address this, we propose enhancements to the PSGAN framework by introducing novel regularization techniques for the generator loss function. Experimental results on images from the Worldview-3 dataset demonstrate that the proposed modifications improve spectral fidelity and achieve superior performance across multiple quantitative metrics while delivering visually superior results.

翻译：全色锐化是遥感领域的一项关键任务，旨在通过融合低分辨率多光谱数据与高分辨率全色图像来生成高分辨率多光谱图像。本文对传统及基于深度学习的全色锐化方法进行了全面分析。尽管最先进的深度学习方法已显著提升了图像质量，但光谱失真等问题依然存在。为解决此问题，我们通过为生成器损失函数引入新颖的正则化技术，对PSGAN框架提出了改进方案。在Worldview-3数据集图像上的实验结果表明，所提出的改进方案提升了光谱保真度，在多项定量指标上实现了更优的性能，同时提供了视觉上更佳的结果。