关键词: 全色锐化, 监督学习, 无监督学习, 图像融合, 遥感图像处理

Abstract: Although deep learning-based pansharpening methods for remote sensing images have made certain progress, most rely on supervised training with downsampled data, which is susceptible to scale bias and struggles to maintain stable performance at full resolution. In contrast, unsupervised methods optimize directly on full-resolution images, avoiding issues caused by downsampling, but generally exhibit weaker robustness due to the lack of explicit supervisory signals. To address this, a pansharpening Network based on Two-stage Collaborative Optimization (TCONet) was proposed. The first stage involves supervised training on downsampled data, combining an improved multi-resolution analysis method with an attention mechanism to improve spatial detail and spectral preservation capabilities. The second stage constructed an unsupervised information compensation network, optimizing directly on full-resolution images to compensate for information loss caused by scale inconsistency. Experimental results indicate that TCONet outperforms other comparative methods in terms of both visual quality and evaluation metrics on three remote sensing datasets.

Key words: pansharpening, supervised learning, unsupervised learning, image fusion, remote sensing image processing