This repo contains code and development for the Land Cover use case, providing spatial coverage of surveyed area, reasonable estimates on atmospheric contributions, and comparisons to a spectral library of known geologic materials.
-src
-utils (containing setup instructions, xml reader etc.)
-lib (earth-sun distance lookup table, etc)
-cal (atmospheric correction and other calibration code)
-classification (land cover classification code)
-entk_scripts (Ensemble pipeline scripts)
-validation_suite (code to check installation and testing results)
The following scripts are used to calibrate raw WorldView-2 and -3 satellite image data into reflectance
rad.py - convert raw digital number tif input to top-of-atmosphere radiance. Output images end with rad.tif
atmcorr_regr.py - uses .txt files of manually collected spectra from an image to run dark object subtraction and regress
ions and creates an output file with band averages representative of the atmosphere
atmcorr_specmath.py - uses the output file from atmcorr_regr.py to atmospherically correct radiance image. Output images
end with rad_atmcorr.tif
refl.py - convert either radiance tif input to top-of-atmosphere reflectance or atmospherically corrected radiance tif i
nput to atmospherically corrected reflectance. Output images end with either rad_refl.tif or rad_atmcorr_refl.tif
Each script requires the same single argument, -ip (or --input_dir), for the input directory.
python rad.py -ip /path/to/input/files
The following scripts are used to classify the reflectance into types of landcover
class.py - create class masks based on spectral properties
shp.py - convert the class masks to shapefiles
Dependancies:
This file may be used to create an environment using:
$ conda create --name --file
platform: linux-64
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