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@ARTICLE{Sedona:902093,
      author       = {Sedona, Rocco and Paris, Claudia and Cavallaro, Gabriele
                      and Bruzzone, Lorenzo and Riedel, Morris},
      title        = {{A} {H}igh-{P}erformance {M}ultispectral {A}daptation {GAN}
                      for {H}armonizing {D}ense {T}ime {S}eries of {L}andsat-8 and
                      {S}entinel-2 {I}mages},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {14},
      issn         = {2151-1535},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-04028},
      pages        = {10134 - 10146},
      year         = {2021},
      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.},
      cin          = {JSC},
      ddc          = {520},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / AISee - AI- and
                      Simulation-Based Engineering at Exascale (951733) / DEEP-EST
                      - DEEP - Extreme Scale Technologies (754304) / ADMIRE -
                      Adaptive multi-tier intelligent data manager for Exascale
                      (956748)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951733 /
                      G:(EU-Grant)754304 / G:(EU-Grant)956748},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000707442300013},
      doi          = {10.1109/JSTARS.2021.3115604},
      url          = {https://juser.fz-juelich.de/record/902093},
}