Digital Signal Processing Reference
In-Depth Information
APPENDIX (A-2)
Atmospheric Correction of Satellite Images
The nature of remote sensing requires that solar radiation pass through the atmosphere before it is
collected by the instrument. Because of this, remotely sensed images include information about the
atmosphere and the earth's surface. For those interested in quantitative analysis of surface reflectance
which is the case for water quality parameters, removing the influence of the atmosphere is a critical
pre-processing step. To compensate for atmospheric effects, properties such as the amount of water
vapour, distribution of aerosols, and scene visibility must be known. Because direct measurements of
these atmospheric properties are rarely available, there are techniques that infer them from their
imprint on hyperspectral radiance data. These properties are then used to constrain highly accurate
models of atmospheric radiation transfer to produce an estimate of the true surface reflectance.
Moreover, atmospheric corrections of this type can be applied on a pixel-by-pixel basis because each
pixel in a hyperspectral image contains an independent measurement of atmospheric water vapor
absorption bands.
ENVI's Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module is a first-
principles atmospheric correction modeling tool for retrieving spectral reflectance from hyperspectral
radiance images. With FLAASH, we can accurately compensate for atmospheric effects. FLAASH
corrects wavelengths in the visible through near-infrared and short-wave infrared regions, up to 3 mm.
(For thermal regions, use the Basic Tools Preprocessing Calibration Utilities Thermal Atm
Correction menu option.) Unlike many other atmospheric correction programs that interpolate
radiation transfer properties from a pre-calculated database of modeling results, FLAASH incorporates
the MODTRAN4 radiation transfer code.
We can choose any of the standard MODTRAN model atmospheres and aerosol types to represent the
scene; a unique MODTRAN solution is computed for each image. FLAASH also includes the
following features: Correction for the adjacency effect (pixel mixing due to scattering of surface-
reflected radiance). An option to compute a scene-average visibility (aerosol/haze amount). FLAASH
uses the most advanced techniques for handling particularly stressing atmospheric conditions, such as
the presence of clouds.
FLAASH supports hyperspectral sensors (such as HyMAP, AVIRIS, HYDICE, HYPERION, Probe-1,
CASI, and AISA) and multispectral sensors (such as Landsat, SPOT, IRS, and ASTER). Water vapor
and aerosol retrieval are only possible when the image contains bands in appropriate wavelength
positions (see Input Data Requirements for details). In addition, FLAASH can correct images
collected in either vertical (nadir) or slant-viewing geometries.
FLAASH was developed by Spectral Sciences, Inc., a world leader in optical phenomenology
research, under the sponsorship of the U.S. Air Force Research Laboratory. Spectral Sciences has been
an integral part in the development of modern atmospheric radiation transfer models, and has worked
extensively on MODTRAN since the model's inception in 1989.
The ENVI FLAASH Model
This section is a brief overview of the atmospheric correction method used by FLAASH.
FLAASH starts from a standard equation for spectral radiance at a sensor pixel, L, that applies to the
solar wavelength range (thermal emission is neglected) and flat, Lambertian materials or their
equivalents. The
equation (1)
is as follows:
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