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APPROACHES FOR
 ATMOSPHERIC
  CORRECTION



       NIRMAL KUMAR
           AP:140
Atmospheric correction
To retrieve surface reflectance from RS imagery
   relationship between radiance
    received at the sensor (above
    atmosphere) and radiance
    leaving the ground

    Ls =H.ρ.T + Lp
 Ls – at sensor radiance
 H – total downwelling radiance
 ρ – reflectance of target
 T – atmospheric transmittance
 Lp – atmospheric path radiance
 (wavelength dependent)
Why do atmospheric correction?
 Physical       relation of radiance to surface
    property (surface normal, surface roughness, reflectance).
       Atmospheric component needs to be removed
   Image ratios (NDVI) leads to biased estimate
       Scattering increases inversely with wavelength
       The involved channels will be unequally affected
 Time difference between image acquisition and
  ground truth measurements
 Comparison of RS data captured at different times
       Conditions may be different
Atmospheric correction methods
 Image   – based methods
     Dark pixel method
     Regression method
 Empirical line method
 Radiative transfer models
 Relative correction method (PIFs)
Dark pixel subtraction method
                   Ls =H.ρ.T + Lp
 Pixel
      values of low reflectance areas
 near zero
     Exposure of dark colored rocks
     Deep shadows
     Clear water
 Lowest pixel values in visible and NIR are
  approximation to atmospheric path
  radiance
 Minimum values subtracted from image
Regression method
 NIR   pixel values are plotted against values
  in other bands
 Apply a straight line using the least square
   method
 If there was no haze, the line would pass
  through origin
 resulting offset is approximation for
  atmospheric path radiance
 offset subtracted from image
Empirical line correction method
  Use target of “known”, low and high reflectance
   targets in one channel e.g. non-turbid water & desert,
   or dense dark vegetation & snow
  Assume radiance, L = gain * DN + offset
  Offset is assumed to be atmospheric part of signal
              Radiance, L

                     Offset assumed to be atmospheric
                     path radiance




                                                                Regression line L = G*DN + O



                                                        Target DN values
                                                        DN
Conversion of DNs to absolute
           radiance value
3   steps
  • Convert DN to apparent radiance Lapp
  • Convert Lapp to apparent reflectance (knowing
    response of sensor)
  • Convert to at-ground reflectance i.e. intrinsic surface
    property by accounting for atmosphere
Use Radiative transfer models
Radiative transfer models
 Limited by the need to supply data about
  atmospheric conditions at time of acquisition
 Mostly used with "standard atmospheres"
 Available numerical models
        􀁸 LOWTRAN 7
        􀁸 MODTRAN 4
        􀁸 ATREM
        􀁸 ATCOR
        􀁸 6S (Second Simulation of the Satellite Signal in
        the solar spectrum)
THANK YOU

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Atmospheric correction

  • 1. APPROACHES FOR ATMOSPHERIC CORRECTION NIRMAL KUMAR AP:140
  • 2. Atmospheric correction To retrieve surface reflectance from RS imagery  relationship between radiance received at the sensor (above atmosphere) and radiance leaving the ground Ls =H.ρ.T + Lp Ls – at sensor radiance H – total downwelling radiance ρ – reflectance of target T – atmospheric transmittance Lp – atmospheric path radiance (wavelength dependent)
  • 3. Why do atmospheric correction?  Physical relation of radiance to surface property (surface normal, surface roughness, reflectance).  Atmospheric component needs to be removed  Image ratios (NDVI) leads to biased estimate  Scattering increases inversely with wavelength  The involved channels will be unequally affected  Time difference between image acquisition and ground truth measurements  Comparison of RS data captured at different times  Conditions may be different
  • 4. Atmospheric correction methods  Image – based methods  Dark pixel method  Regression method  Empirical line method  Radiative transfer models  Relative correction method (PIFs)
  • 5. Dark pixel subtraction method Ls =H.ρ.T + Lp  Pixel values of low reflectance areas near zero  Exposure of dark colored rocks  Deep shadows  Clear water  Lowest pixel values in visible and NIR are approximation to atmospheric path radiance  Minimum values subtracted from image
  • 6. Regression method  NIR pixel values are plotted against values in other bands  Apply a straight line using the least square method  If there was no haze, the line would pass through origin  resulting offset is approximation for atmospheric path radiance  offset subtracted from image
  • 7. Empirical line correction method  Use target of “known”, low and high reflectance targets in one channel e.g. non-turbid water & desert, or dense dark vegetation & snow  Assume radiance, L = gain * DN + offset  Offset is assumed to be atmospheric part of signal Radiance, L Offset assumed to be atmospheric path radiance Regression line L = G*DN + O Target DN values DN
  • 8. Conversion of DNs to absolute radiance value 3 steps • Convert DN to apparent radiance Lapp • Convert Lapp to apparent reflectance (knowing response of sensor) • Convert to at-ground reflectance i.e. intrinsic surface property by accounting for atmosphere Use Radiative transfer models
  • 9. Radiative transfer models  Limited by the need to supply data about atmospheric conditions at time of acquisition  Mostly used with "standard atmospheres"  Available numerical models  􀁸 LOWTRAN 7  􀁸 MODTRAN 4  􀁸 ATREM  􀁸 ATCOR  􀁸 6S (Second Simulation of the Satellite Signal in the solar spectrum)