Journal Article

FZJ-2021-04028

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images

Sedona, R.FZJ* ; Paris, C. ; Cavallaro, G. (Corresponding author)FZJ* ; Bruzzone, L. ; Riedel, M.FZJ*

2021
IEEE New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/28855  doi:10.1109/JSTARS.2021.3115604

Abstract: 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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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. AISee - AI- and Simulation-Based Engineering at Exascale (951733) (951733)
3. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)
4. ADMIRE - Adaptive multi-tier intelligent data manager for Exascale (956748) (956748)

Appears in the scientific report 2021

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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