• DocumentCode
    2131090
  • Title

    An evaluation of atmospheric correction techniques using the spectral similarity scale

  • Author

    Granahan, J.C. ; Sweet, J.N.

  • Author_Institution
    Bae Syst. Mission Solutions, San Diego, CA, USA
  • Volume
    5
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    2022
  • Abstract
    The spectral similarity scale (SSS) is used to evaluate the ATREM and ACORN hyperspectral atmospheric correction software techniques. The SSS evaluates spectra by evaluating the shape and the brightness between pairs of spectra in a hyperspectral data set. An AVIRIS observation of corn crops in Shelton, Nebraska with ground truth is used for this evaluation. Initial results show that it is possible for atmospheric correction techniques to add many "false" spectral features that were not present in the original observation. A correct atmospheric correction of a data set increases the spectral contrast of some data and reveals other subtle spectral features. The ACORN software provides a superior correction to ATREM in terms of removing gaseous spectral features such as that of water
  • Keywords
    geophysical signal processing; geophysical techniques; geophysics computing; multidimensional signal processing; remote sensing; vegetation mapping; ACORN; ATREM; IR; agriculture; atmosphere; atmospheric correction; crops; false spectral features; hyperspectral remote sensing; infrared; measurement technique; multispectral remote sensing; optics; satellite remote sensing; software; spectral similarity scale; vegetation mapping; visible; Brightness; Crops; Data mining; Euclidean distance; Hyperspectral imaging; Niobium; Reflectivity; Shape measurement; Software tools; Spectral shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    0-7803-7031-7
  • Type

    conf

  • DOI
    10.1109/IGARSS.2001.977890
  • Filename
    977890