SIRAN: Sinkhorn Distance Regularized Adversarial Network for DEM
  Super-resolution using Discriminative Spatial Self-attention
Subhajit Paul Ashutosh Gupta
Digital Elevation Model (DEM) is an essential aspect in the remote sensing
domain to analyze and explore different applications related to surface
elevation information. In this study, we intend to address the generation of
high-resolution DEMs using high-resolution multi-spectral (MX) satellite
imagery by incorporating adversarial learning. To promptly regulate this
process, we utilize the notion of polarized self-attention of discriminator
spatial maps as well as introduce a Densely connected Multi-Residual Block
(DMRB) module to assist in efficient gradient flow. Further, we present an
objective function related to optimizing Sinkhorn distance with traditional GAN
to improve the stability of adversarial learning. In this regard, we provide
both theoretical and empirical substantiation of better performance in terms of
vanishing gradient issues and numerical convergence. We demonstrate both
qualitative and quantitative outcomes with available state-of-the-art methods.
Based on our experiments on DEM datasets of Shuttle Radar Topographic Mission
(SRTM) and Cartosat-1, we show that the proposed model performs preferably
against other learning-based state-of-the-art methods. We also generate and
visualize several high-resolution DEMs covering terrains with diverse
signatures to show the performance of our model.