Sentinel-2 and SPOT-7 Images in Machine Learning Frameworks for Super-Resolution.

Monitoring construction sites from space using high-resolution (HR) imagery enables remote tracking instead of physically traveling to a site. Thus, valuable resources are saved while recording of the construction site progression at anytime and anywhere in the world is feasible. In the present work Sentinel-2 (S2) images at 10 m (m) are spatially super-resolved per factor 4 by means of deep-learning. Initially, the very-deep super-resolution (VDSR) network is trained with matching pairs of S2 and SPOT-7 images at 2.5 m target resolution. Then, the trained VDSR network, named SPOT7-VDSR, becomes able to increase the resolution of S2 images which are completely unknown to the net. Additionally, the VDSR net technique and bicubic interpolation are applied to increase the resolution of S2. Numerical and visual comparisons are carried out on the area of interest Karditsa, Greece. The current study of super-resolving S2 images is novel in the literature and can prove very useful in application cases where only S2 images are available and not the corresponding SPOT-7 higher-resolution ones. During the present super-resolution (SR) experimentations, the proposed net SPOT7-VDSR outperforms the VDSR net up to 8.24decibel in peak signal to noise ratio (PSNR) and bicubic interpolation up to 16.9% in structural similarity index (SSIM).