A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images

Abstract

The combination of data acquired by Landsat-8 and Sentinel-2 Earth Observation (EO) missions produces dense Time Series (TSs) of multispectral images that are essential for monitoring the dynamics of land-cover and land-use classes across the Earths surface with high temporal resolution. However, the optical sensors of the two missions have different spectral and spatial properties, thus they require a harmonization processing step before they can be exploited in Remote Sensing (RS) applications. In this work, we propose a workflow based on a Deep Learning (DL) approach to harmonize these two products developed and deployed on an High-Performance Computing (HPC) environment. In particular, we use a multispectral Generative Adversarial Network (GAN) with a U-Net generator and a PatchGan discriminator to integrate existing Landsat-8 TSs with data sensed by the Sentinel-2 mission. We show a qualitative and quantitative comparison with an existing physical method (NASA Harmonized Landsat and Sentinel (HLS)) and analyze original and generated data in different experimental setups with the support of spectral distortion metrics. To demonstrate the effectiveness of the proposed approach, a crop type mapping task is addressed using the harmonized dense TS of images, which achieved an Overall Accuracy (OA) of 87.83% compared to 81.66% of the state-of-the-art method.