ATCOR-3-Satellite
Atmospheric & Topographic Correction for Small and Medium FOV Satellite Imagery for Rugged Terrain.
The ATCOR software derives surface reflectance, emissivity, and temperature from calibrated images by atmospheric and topographic correction. The model is applicable to all optical remote sensing systems with special focus on imaging spectroscopy data. ATCOR is the major, state of the art software suite available on the market which includes the capability for radiometric correction in rugged terrain considering cast shadow and illumination calculations.
Description
The software offers these key advantages:
- Atmospheric database (look-up-tables of radiative transfer calculations with Modtran®-5) covering a wide range of weather conditions and sun angles.
- Image processing with constant atmospheric conditions or spatially varying aerosol conditions (the latter is retrieved from image if near- or short wave infrared bands are available).
- Included module to determine atmospheric parameters (aerosol type, visibility, water vapor). This can be done by comparing retrieved scene reflectance spectra of various surface covers with library spectra as a function of the selected atmospheric parameter.
- The atmospheric database includes a wide range of pre-calculated radiative transfer runs for different weather conditions and sun angles employing the Modtran®-5 code (DISORT 8-stream option for multiple scattering).
- Capability for inflight radiometric calibration: for known atmospheric parameters and known target surface reflectances the radiometric calibration coefficients can be calculated. This corresponds to the improved method of empirical line fit because it includes the adjacency effect.
- Visibility estimate prior to scene processing (interactive mode)
- Aerosol type estimate prior to scene processing (interactive mode)
- Retrieval of atmospheric water vapor column for sensors with water vapor bands (around 940/1130 nm). Example sensors: MOS-B, Hyperion.
- Statistical haze removal: a fully automatic algorithm that masks haze and cloud regions and removes haze of land areas (low altitude haze).
- Cirrus cloud removal (requires a narrow 1.38 micron band).
- De-shadowing of cloud or building shadow areas, using image-based shadow detection routines.
- Automatic classification of spectral surface reflectance (program SPECL2) using 10 surface cover templates. This is nota land use classification, but a reflectance-shape classification. Still, it may be useful as it is a fast automatic classification algorithm.
- Surface emissivity and surface (brightness) temperature outputs for thermal band sensors.
- Spectral polishing of reflectance cube (only hyperspectral imagery).
- Value added products in a separate file (16 bit integer) : vegetation index SAVI, LAI, FPAR, wavelength-integrated albedo, absorbed solar radiation flux. In addition, surface energy fluxes for thermal band sensors can be calculated: net radiation, ground heat flux, latent heat, sensible heat flux.
- Correction of spectral "smile" supported (hyperspectral instruments).
- Correction of observation BRDF effects using the BREFCOR method. The method is applicable to satellite imagery if a number of images is acquired under varying observation angles in order to improve mosaicking results.
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Specifications
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Application
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Main users
Data provider and surveyors, data analysis specialists.
Main applications
Scientific data analysis, high accuracy spectral processing.
Distinguishable features
Atmospheric and topographic correction of small and wide FOV satellite imagery, operational BRDF effects correction (BREFCOR), MODTRAN-6 radiative transfer code based automatic processing.
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General
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Year of introduction
2001
Year of last update
2021
Supported hardware
Windows, Linux, MacOSX, suitable for all kind of airborne multispectral, thermal and hyperspectral sensors
User definable operations and extensions
Batch processing
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Radiometric and image processing
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Radiometric corrections
Atmospheric correction, reflectance retrieval, topographic correction, BRDF correction
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Pixel-based classification
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Unsupervised classification
Y
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Geometric processing
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Resampling
Y