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@ARTICLE{Bazi:911346,
      author       = {Bazi, Yakoub and Cavallaro, Gabriele and Demir, Begüm and
                      Melgani, Farid},
      title        = {{L}earning from {D}ata for {R}emote {S}ensing {I}mage
                      {A}nalysis},
      journal      = {International journal of remote sensing},
      volume       = {43},
      number       = {15-16},
      issn         = {0143-1161},
      address      = {London},
      publisher    = {Taylor $\&$ Francis},
      reportid     = {FZJ-2022-04638},
      pages        = {5527 - 5533},
      year         = {2022},
      abstract     = {Recent advances in satellite technology have led to a
                      regular, frequent and high- resolution monitoring of Earth
                      at the global scale, providing an unprecedented amount of
                      Earth observation (EO) data. The growing operational
                      capability of global Earth monitoring from space provides a
                      wealth of information on the state of our planet Earth that
                      waits to be mined for several different EO applications,
                      e.g. climate change analysis, urban area studies, forestry
                      applications, risk and damage assessment, water quality
                      assessment, crop monitoring and so on. Recent studies in
                      machine learning have triggered substantial performance gain
                      for the above-mentioned tasks. Advanced machine learning
                      models such as deep convolutional neural networks (CNNs),
                      recursive neural networks and transformers have recently
                      made great progress in a wide range of remote sensing (RS)
                      tasks, such as object detection, RS image classification,
                      image captioning and so on. The study of Bai et al. (2021)
                      analyzes the research progress, hotspots, trends and methods
                      in the field of deep learning in remote sensing, and deep
                      learning is becoming an important tool for remote sensing
                      and has been widely used in numerous remote sensing tasks
                      related to image processing and analysis. In this context,
                      the present special issue aims at gathering a collection of
                      papers in the most advanced and trendy areas dealing with
                      learning from data and with applications to remote sensing
                      image analysis. The manuscripts can be subdivided into five
                      groups depending mainly on the processing or learning task.
                      A specific collection for hyperspectral imagery has been
                      included given the special attention by the remote sensing
                      community to this kind of data.},
      cin          = {JSC},
      ddc          = {620},
      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)16},
      UT           = {WOS:000882850800001},
      doi          = {10.1080/01431161.2022.2131481},
      url          = {https://juser.fz-juelich.de/record/911346},
}