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@INBOOK{Sedona:1029396,
      author       = {Sedona, Rocco and Cavallaro, Gabriele and Riedel, Morris
                      and Benediktsson, Jón Atli},
      title        = {{P}roven {A}pproaches of {U}sing {I}nnovative
                      {H}igh-{P}erformance {C}omputing {A}rchitectures in {R}emote
                      {S}ensing},
      address      = {Boca Raton},
      publisher    = {CRC Press},
      reportid     = {FZJ-2024-05104},
      isbn         = {9781003382010},
      pages        = {432},
      year         = {2024},
      comment      = {Signal and Image Processing for Remote Sensing},
      booktitle     = {Signal and Image Processing for Remote
                       Sensing},
      abstract     = {This chapter underscores the essential role of
                      high-performance computing (HPC) in the realm of remote
                      sensing (RS), effectively addressing the growing demand for
                      processing extensive and complex datasets. HPC, empowered by
                      parallel programming paradigms, significantly speeds up a
                      range of tasks, including image processing, data mining, and
                      modeling, vital in the context of Earth observation (EO)
                      applications. More notably, HPC can build even better models
                      by employing systematic hyperparameter optimization methods
                      that are computationally demanding, given a large search
                      space. Furthermore, with deep learning (DL) progressively
                      gravitating toward foundation models, extensively trained on
                      substantial datasets, endowing them with the remarkable
                      capability to transfer knowledge across diverse tasks, there
                      is an increased demand for computational resources in the
                      fast-paced landscape of artificial intelligence (AI) and
                      consequently a heightened interest in HPC. Solutions to
                      provide optimized resources on HPC resources, however, have
                      increased their complexity and heterogeneity. This chapter
                      highlights the advantages of embracing HPC while
                      acknowledging current challenges, solutions, and future
                      trends.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/1029396},
}