Home > Publications database > A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images > print

001     902093
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024 7 _ |a 10.1109/JSTARS.2021.3115604
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100 1 _ |a Sedona, Rocco
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245 _ _ |a A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images
260 _ _ |a New York, NY
|c 2021
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520 _ _ |a 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.
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700 1 _ |a Paris, Claudia
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700 1 _ |a Cavallaro, Gabriele
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700 1 _ |a Bruzzone, Lorenzo
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700 1 _ |a Riedel, Morris
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773 _ _ |a 10.1109/JSTARS.2021.3115604
|g Vol. 14, p. 10134 - 10146
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|t IEEE journal of selected topics in applied earth observations and remote sensing
|v 14
|y 2021
|x 2151-1535
856 4 _ |u https://juser.fz-juelich.de/record/902093/files/Invoice_APC600261810_.pdf
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