用几何引导网络融合10m与20m遥感影像，提升分辨率细节。
Super-Resolution of Sentinel-2 Images Using a Geometry-Guided Back-Projection Network with Self-Attention
- 基于10m波段生成几何引导图，指导超分辨重建。
- 在城市、乡村、海岸三类场景中均优于传统与深度学习方法。
- 引入多头自注意力建模跨空间谱域非局部相似性。
哨兵-2任务提供13个波段的多光谱影像，分辨率为10米、20米和60米。其中10米波段呈现精细结构细节，20米波段包含更丰富的光谱信息。本文提出一种几何引导的超分辨率模型，用于融合10米与20米波段数据。方法通过聚类学习生成高几何信息的引导图像，嵌入到展开式反投影架构中，利用多头自注意力机制建模跨空间与光谱维度的非局部块间相似性。同时构建了评估数据集，包含城市、乡村和海岸三类测试集。实验表明，该方法在多个指标上均优于经典及深度学习超分辨与融合技术。
原文摘要 · Abstract (English)
The Sentinel-2 mission provides multispectral imagery with 13 bands at resolutions of 10m, 20m, and 60m. In particular, the 10m bands offer fine structural detail, while the 20m bands capture richer spectral information. In this paper, we propose a geometry-guided super-resolution model for fusing the 10m and 20m bands. Our approach introduces a cluster-based learning procedure to generate a geometry-rich guiding image from the 10m bands. This image is integrated into an unfolded back-projection architecture that leverages image self-similarities through a multi-head attention mechanism, which models nonlocal patch-based interactions across spatial and spectral dimensions. We also generate a dataset for evaluation, comprising three testing sets that include urban, rural, and coastal landscapes. Experimental results demonstrate that our method outperforms both classical and deep learning-based super-resolution and fusion techniques.
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