改进GAN模型，提升遥感图像融合的光谱保真度
Comprehensive Analysis and Improvements in Pansharpening Using Deep Learning
- 在PSGAN框架中引入新型正则化损失，优化生成器训练
- 在WorldView-3数据集上显著降低光谱失真，多指标领先
- 适合遥感图像处理、高分辨率融合研究者参考
影像融合是遥感领域的重要任务，通过融合低分辨率多光谱图像与高分辨率全色图像，生成高分辨率多光谱图像。本文对传统及基于深度学习的融合方法进行了全面分析。尽管先进深度学习方法显著提升了图像质量，但光谱失真问题依然存在。为此，我们针对PSGAN框架提出改进，引入新的生成器损失函数正则化技术。在WorldView-3数据集上的实验表明，所提方法有效提升光谱保真度，在多个定量评估指标上表现更优，并呈现更佳视觉效果。
原文摘要 · Abstract (English)
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.
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