Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks

Massimiliano Gargiulo,Giuseppe Scarpa,Antonio Mazza,Giuseppe Ruello,Raffaele Gaetano

doi:10.3390/rs11222635

Abstract

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.

Highlights

The twin Sentinel-2 satellites ensure a global World coverage with a revisit time of five days at the equator, providing a multi-resolution stack composed of 13 spectral bands, between the visible and short-wave infrared (SWIR), distributed over three resolution levels
Beyond land-cover classification, S2 images can be useful in such diverse applications as the prediction of growing stock volume in forest ecosystems [3], the estimation of the Leaf Area Index (LAI) [4,5], the retrieval of canopy chlorophyll content [6], the mapping of the extent of glaciers [7], the water quality monitoring [8], the classification of crop or tree species [9], and the built-up areas detection [10]
We presented and validated experimentally a new Convolutional Neural Networks (CNNs)-based super-resolution method for the 20 m bands of Sentinel-2 images, which blends high-resolution spatial information from the 10 m bands of the same sensor

Summary

Introduction

The twin Sentinel-2 satellites ensure a global World coverage with a revisit time of five days at the equator, providing a multi-resolution stack composed of 13 spectral bands, between the visible and short-wave infrared (SWIR), distributed over three resolution levels. Beyond land-cover classification, S2 images can be useful in such diverse applications as the prediction of growing stock volume in forest ecosystems [3], the estimation of the Leaf Area Index (LAI) [4,5], the retrieval of canopy chlorophyll content [6], the mapping of the extent of glaciers [7], the water quality monitoring [8], the classification of crop or tree species [9], and the built-up areas detection [10]. In light of its free availability, world-wide coverage, revisit frequency and, not least, its above remarked wide applicability, several research teams have proposed solutions to super-resolve Sentinel-2 images, rising 20 m and/or 60 m bands up to 10 m resolution. Several works testify the advantage of using super-resolved S2 images in several applications such as water mapping [11], fire detection [12], urban mapping [13], and vegetation monitoring [14]

Methods

Results

Discussion

Conclusion