A spatially adaptive fast atmospheric correction algorithm
- 27 April 1996
- journal article
- research article
- Published by Taylor & Francis in International Journal of Remote Sensing
- Vol. 17 (6) , 1201-1214
- https://doi.org/10.1080/01431169608949077
Abstract
An atmospheric correction algorithm for high spatial resolution satellite sensors like Landsat Thematic Mapper (TM) has been developed. The algorithm works with a catalogue of atmospheric correction functions stored in look-up tables. The catalogue consists of a broad range of atmospheric conditions (different altitude profiles of pressure, air temperature, and humidity; several aerosol types; ground elevations from 0-1 km above sea level; solar zenith angles ranging from 0°-70°). The catalogue covers visibilities (surface meteorological range) from 5-40 km, values can be extrapolated down to 4 km and up to 80km. The 1994 edition of the catalogue was compiled using the MODTRAN-2 and the SENSAT-5 codes. The algorithm consists of an interactive and an automatic part. The interactive phase serves for the definition of a reference target (dense dark vegetation or water) as well as haze and cloud. The reflectance of the reference target in a single spectral band (dark vegetation: TM band 3; water: TM band 4) has to be specified. Additionally, the image can be partitioned into sub-images, called sectors. This phase also selects one of the atmospheres available in the catalogue, i.e. the altitude profile of pressure, temperature and humidity as well as the aerosol type (e.g. rural) are fixed. The automatic phase first calculates the visibility of the reference areas for the selected atmosphere. The visibility is obtained by matching the measured signal (i.e the digital number (DN) converted to a radiance using the sensor calibration) to the model-derived signal in the spectral channel of known target reflectance. The sector-average visibility of the reference pixels is assigned to the non-reference pixels. The second step is the haze removal performed by histogram matching the statistics of the haze regions to the statistics of the clear part of the scene for each sector and each channel. The last step is the calculation of the ground reflectance image including the adjacency correction, and the computation of the ground brightness temperature image (TM band 6)Keywords
This publication has 22 references indexed in Scilit:
- High resolution imaging spectrometer (Hiris): Science and instrumentInternational Journal of Imaging Systems and Technology, 1991
- A fast atmospheric correction algorithm applied to Landsat TM imagesInternational Journal of Remote Sensing, 1990
- MOMS-02 sensor simulation and spectral band selectionInternational Journal of Remote Sensing, 1989
- Calibration comparison for the Lands at 4 and 5 multispectral scanners and thematic mappersApplied Optics, 1989
- Algorithm for automatic atmospheric corrections to visible and near-IR satellite imageryInternational Journal of Remote Sensing, 1988
- Brightness temperature algorithms for landsat thematic mapper dataRemote Sensing of Environment, 1988
- Soil and Sun angle interactions on partial canopy spectraInternational Journal of Remote Sensing, 1987
- Multiple scattering lowtran and fascode modelsApplied Optics, 1987
- Dual channel satellite measurements of sea surface temperatureQuarterly Journal of the Royal Meteorological Society, 1983
- Estimation of sea surface temperature from spaceRemote Sensing of Environment, 1970