Sentinel-2 60-m Band Super-Resolution Using Hybrid CNN-GPR Model

Abstract

Sentinel-2 image super-resolution (SR) has proven advantageous in multiple data analysis pipelines, leading to a more comprehensive assessment of different environment-related metrics. This research aims to provide a method for super-resolving the 60-m bands provided by Sentinel-2 up to 10-m spatial resolution, using Gaussian process regression (GPR). While common GPR methods directly operate on raw data using carefully designed kernels, we propose a convolutional neural network (CNN)-based feature extraction kernel to directly process the input 10-m patches, applied in constructing the elements of the integrated covariance matrices. For each scene, a small number of training patches are sampled to optimize the CNN parameters and to construct the predictive mean function, the latter being further used for predicting super-resolved pixels for new input areas. We prove that our method is a reliable SR mechanism by assessing its performance both quantitatively, using metrics against other methods from literature, and qualitatively, through visual analysis of the results.