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 (HR) DEMs guided by HR multi-spectral (MX) satellite imagery as prior. To promptly regulate this process, we utilize the discriminator activations as spatial attention for the MX prior, and also introduce a Densely connected Multi-Residual Block (DMRB) module to assist in efficient gradient flow. Further, we present the notion of using 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 benchmark methods. We also generate and visualize several high-resolution DEMs covering terrains with diverse signatures to show the performance of our model.