Sentinel-2 Images at 2.5m Spatial Resolution via Deep-Learning: A Case Study in Zakythnos

Antigoni Panagiotopoulou, Emmanuel Bratsolis, Lazaros Grammatikopoulos, Eleni Petsa, Eleni Charou, Konstantinos Poirazidis, Aristotelis Martinis, Nicholas Madamopoulos

Abstract: High-resolution (HR) satellite images can provide detailed information about land usage/land cover. Often, it is necessary that the satellite sensor inherent spatial resolution is increased through algorithmic processing of the image data acquired. Machine-learning and in particular deep-learning based super-resolution (SR) techniques are an effective tool for increasing the spatial resolution of images. In the current work, Sentinel-2 images are super-resolved to spatial resolution equal to 2.5 m/pixel by means of deep-learning based SR techniques. The area of study is Zakynthos island in Greece. A novel index called Normalized Carotenoid Reflectance Index (NCRI) is proposed for the assessment of land cover by olive trees.