TY  - JOUR
AU  - Sedona, Rocco
AU  - Paris, Claudia
AU  - Cavallaro, Gabriele
AU  - Bruzzone, Lorenzo
AU  - Riedel, Morris
TI  - A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images
JO  - IEEE journal of selected topics in applied earth observations and remote sensing
VL  - 14
SN  - 2151-1535
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2021-04028
SP  - 10134 - 10146
PY  - 2021
AB  - The combination of data acquired by Landsat-8 and Sentinel-2 earth observation 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 earth's 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 applications. In this work, we propose a workflow-based on a deep learning approach to harmonize these two products developed and deployed on an high-performance computing environment. In particular, we use a multispectral generative adversarial network 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 [National Aeronautics and Space Administration (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 of 87.83% compared to 81.66% of the state-of-the-art method.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000707442300013
DO  - DOI:10.1109/JSTARS.2021.3115604
UR  - https://juser.fz-juelich.de/record/902093
ER  -