Atmospheric Correction

Before Correction After Correction

TM imagery, color-composite (bands 1, 2, and 3).

Remotely sensed imagery has been used for developing and validating various studies regarding land cover dynamics such as global carbon modeling, biogeochemical cycling, hydrological modeling, and ecosystem response modeling. However, the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles through absorption and scattering of the radiation from the earth surface. The objective of atmospheric correction is to retrieve the surface reflectance (that characterizes the surface properties) from remotely sensed imagery by removing the atmospheric effects. Atmospheric correction has been shown to significantly improve the accuracy of image classification.

This problem has received a considerable attention from researchers in remote sensing who have devised a number of solution approaches. Sophisticated approaches are computationally demanding and have only been validated on a very small scale. Atmospheric correction algorithms basically consist of two major steps. First, the optical characteristics of the atmosphere are estimated either by using special features of the ground surface or by direct measurements of the atmospheric constituents or by using theoretical models. Various quantities related to the atmospheric correction can then be computed by the radiative transfer algorithms given the atmospheric optical properties. Second, the remotely sensed imagery can be corrected by inversion procedures that derive the surface reflectance.

We have developed an efficient algorithm to estimate the optical characteristics of the Thematic Mapper (TM) imagery and to remove the atmospheric effects from it. Our algorithm is designed based on look-up table approach and requires less than 60 minutes to correct standard TM image with 50 M Pixels per band (300 M Pixels total) on an IBM RS6000 workstation.

Band 1, Before Correction Band 1, After Correction

Band 2, Before Correction Band 2, After Correction

Band 3, Before Correction Band 3, After Correction

TM imagery (512 X 512).

Before Correction After Correction

TM imagery, color-composite (bands 1, 2, and 4).

We have also developed a parallel version of our algorithm that is scalable, I/O optimal, portable and has been run on a variety of platforms, including an IBM SP-1, an IBM SP-2, a Thinking Machine CM-5, and an Intel Paragon.

Current Areas of Research:



Faculty Research Assistant:

  • Dr. Satya Kalluri

Graduate Students:

last updated 11/3/95

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