SIRAN: Sinkhorn Distance Regularized Adversarial Network for DEM Super-resolution using Discriminative Spatial Self-attention

• URL: http://arxiv.org/abs/2311.16490v1
• Date: Mon, 27 Nov 2023 12:03:22 GMT
• Title: SIRAN: Sinkhorn Distance Regularized Adversarial Network for DEM Super-resolution using Discriminative Spatial Self-attention
• Authors: Subhajit Paul, Ashutosh Gupta
• Abstract summary: 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. We present an objective function related to optimizing Sinkhorn distance with traditional GAN to improve the stability of adversarial learning.
• Score: 5.178465447325005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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