• DocumentCode
    532281
  • Title

    Aerosol retrieval from remote sensing image using artificial neural network

  • Author

    Wang, Houmao ; Tang, Jiakui

  • Author_Institution
    Yantai Inst. of Coastal Zone Res., Chinese Acad. of Sci., Yantai, China
  • Volume
    5
  • fYear
    2010
  • fDate
    22-24 Oct. 2010
  • Abstract
    The usual method of aerosol retrieval using remote sensing is interpolation of look-up-table (LUT), but it is too time-consuming. However, artificial neural network for nonlinear problem has been not applied widely for aerosol retrieval before. In this paper, aerosol optical depth (AOD) is retrieved using two methods: interpolation and neural network. Then, the retrieval capabilities of the two methods were compared. By comparison, not only is the retrieval error of the neural network within acceptable range, but also it can reduce much processing time.
  • Keywords
    aerosols; artificial intelligence; atmospheric techniques; geophysical image processing; image retrieval; interpolation; neural nets; remote sensing; table lookup; LUT; aerosol optical depth; aerosol retrieval; artificial neural network; interpolation; look-up-table; nonlinear problem; remote sensing image; Atmospheric measurements; Data models; Measurement uncertainty; Neurons; Particle measurements; Table lookup; Aerosol optical depth; artificial neural network; interpolation; look-uptables; remote sensing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Application and System Modeling (ICCASM), 2010 International Conference on
  • Conference_Location
    Taiyuan
  • Print_ISBN
    978-1-4244-7235-2
  • Electronic_ISBN
    978-1-4244-7237-6
  • Type

    conf

  • DOI
    10.1109/ICCASM.2010.5620271
  • Filename
    5620271