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@INPROCEEDINGS{Sedona:909754,
      author       = {Sedona, Rocco and Paris, Claudia and Tian, Liang and
                      Riedel, Morris and Cavallaro, Gabriele},
      title        = {{A}n {A}utomatic {A}pproach for the production of a {T}ime
                      {S}eries of {C}onsistent {L}and-cover {M}aps {B}ased on
                      {L}ong-short {T}erm {M}emory},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03385},
      pages        = {203-206},
      year         = {2022},
      abstract     = {This paper presents an approach that aims to produce a
                      Time-Series (TS) of consistent Land-Cover (LC) maps,
                      typically needed to perform environmental monitoring. First,
                      it creates an annual training set for each TS to be
                      classified, leveraging on publicly available thematic
                      products. These annual training sets are then used to
                      generate a set of preliminary LC maps that allow for the
                      identification of the unchanged areas, i.e., the stable
                      temporal component. Such areas can be used to define an
                      informative and reliable multi-year training set, by
                      selecting samples belonging to the different years for all
                      the classes. The multi-year training set is finally employed
                      to train a unique multi-year Long Short Term Memory (LSTM)
                      model, which enhances the consistency of the annual LC maps.
                      The preliminary results carried out on three TSs of Sentinel
                      2 images acquired in Italy in 2018, 2019 and 2020
                      demonstrates the capability of the method to improve the
                      consistency of the annual LC maps. The agreement of the
                      obtained maps is ≈ $78\%,$ compared to the ≈ $74\%$
                      achieved by the LSTM models trained separately.},
      month         = {Jul},
      date          = {2022-07-17},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium (IGARSS),
                       Kuala Lumpur (Malaysia), 17 Jul 2022 -
                       22 Jul 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5112 - Cross-Domain
                      Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
                      (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000920916600051},
      doi          = {10.1109/IGARSS46834.2022.9883655},
      url          = {https://juser.fz-juelich.de/record/909754},
}