用高分辨率卫星图生成更清晰的地形图，效果优于现有方法。
A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformer
- 结合残差块与频域注意力图网络，提升图像引导建模能力。
- 引入Sinkhorn正则化对抗损失，解决梯度消失和收敛难题。
- 在4个数据集上验证，细节更锐利，误差更小，适合遥感地形重建。
数字高程模型（DEM）是遥感领域分析地表高程的重要基础数据。本文提出一种新型混合变换器模型，通过高分辨率多光谱（MX）卫星影像作为引导，生成高分辨率DEM。该模型由密集连接多残差块（DMRB）和多头频域选择图注意力（M-FSGA）构成，并引入判别器空间图作为条件注意力机制以加速优化过程。进一步设计了一种基于Sinkhorn距离的新型对抗目标函数，从理论与实证两方面证明其在缓解梯度消失问题及提升数值收敛性方面的优势。在4个不同DEM数据集上的实验表明，所提方法在定性与定量对比中均优于现有基线方法，生成结果具有更清晰细节与更小误差。
原文摘要 · Abstract (English)
Digital Elevation Model (DEM) is an essential aspect in the remote sensing (RS) domain to analyze various applications related to surface elevations. Here, we address the generation of high-resolution (HR) DEMs using HR multi-spectral (MX) satellite imagery as a guide by introducing a novel hybrid transformer model consisting of Densely connected Multi-Residual Block (DMRB) and multi-headed Frequency Selective Graph Attention (M-FSGA). To promptly regulate this process, we utilize the notion of discriminator spatial maps as the conditional attention to the MX guide. Further, we present a novel adversarial objective related to optimizing Sinkhorn distance with classical GAN. In this regard, we provide both theoretical and empirical substantiation of better performance in terms of vanishing gradient issues and numerical convergence. Based on our experiments on 4 different DEM datasets, we demonstrate both qualitative and quantitative comparisons with available baseline methods and show that the performance of our proposed model is superior to others with sharper details and minimal errors.
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