Li Qingping, Yang Xiaomin, Li Bingru, Wang Jin
College of Electronic Information, Sichuan University, Chengdu 610017, China.
Sensors (Basel). 2025 Apr 18;25(8):2560. doi: 10.3390/s25082560.
Pansharpening techniques are crucial in remote sensing image processing, with deep learning emerging as the mainstream solution. In this paper, the pansharpening problem is formulated as two optimization subproblems with a solution proposed based on multiscale contrastive learning combined with attention-guided gradient projection networks. First, an efficient and generalized Spectral-Spatial Universal Module (SSUM) is designed and applied to spectral and spatial enhancement modules (SpeEB and SpaEB). Then, the multiscale high-frequency features of PAN and MS images are extracted using discrete wavelet transform (DWT). These features are combined with contrastive learning and residual connection to progressively balance spectral and spatial information. Finally, high-resolution multispectral images are generated through multiple iterations. Experimental results verify that the proposed method outperforms existing approaches in both visual quality and quantitative evaluation metrics.
全色锐化技术在遥感图像处理中至关重要，深度学习已成为主流解决方案。本文将全色锐化问题表述为两个优化子问题，并提出了一种基于多尺度对比学习结合注意力引导梯度投影网络的解决方案。首先，设计了一种高效且通用的光谱-空间通用模块（SSUM），并将其应用于光谱和空间增强模块（SpeEB和SpaEB）。然后，使用离散小波变换（DWT）提取PAN和MS图像的多尺度高频特征。这些特征与对比学习和残差连接相结合，以逐步平衡光谱和空间信息。最后，通过多次迭代生成高分辨率多光谱图像。实验结果验证了所提方法在视觉质量和定量评估指标方面均优于现有方法。
