{'fraction_non_alphanumeric': 0.03398058252427184, 'fraction_numerical': 0.007281553398058253, 'mean_word_length': 5.021897810218978, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Sentinel-2 Images at 2.5m Spatial Resolution via Deep-Learning: A Case Study in Zakythnos

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.