Deep learning in remote sensing applications: A meta-analysis and review

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image…

• AV
  Alexander von Humboldt-Stiftung
• NN
  National Natural Science Foundation of China

  Awards: 41701374, 61701160, 41601366
• GO
  Government of Jiangsu Province
• NS
  Natural Science Foundation of Jiangsu Province

  Award: BK20170640