Automatic Relative Radiometric Normalization
ArrNorm is a command line program for apply the radiometric normalization to the target image based on reference image using the IR-MAD algorithm to locate invariant/variant pixels for a relative radiometric normalization.
The algorithm takes advantage of the linear and affine invariance of the Multivariate alteration detection (MAD)
transformation to perform a relative radiometric normalization of the images involved in the transformation, using the correlation of the iteratively reweighted MAD (IR-MAD) [1]
Stop condition is set by max iteration or with a minimum no-change probability threshold. With more iterations the algorithm try to find a better match to the reference image, decreasing the delta, the plugin select the best delta for the final result. However, after several iterations the changes in the delta are imperceptible.
[1] M. J. Canty (2014): Image Analysis, Classification and Change Detection in Remote Sensing, with Algorithms for ENVI/IDL and Python (Third Revised Edition), Taylor and Francis CRC Press.
Check the ArrNorm-Qgis-processing implementation of Arrnorm as a Qgis processing.
For example with Anaconda/Conda environment:
conda install -c conda-forge gdal numpy scipy matplotlib
pip install https://github.com/SMByC/ArrNorm/archive/master.zip
For other parameters check the help:
$ arrnorm -h
Examples:
$ arrnorm -ref reference.tif target.tif
$ arrnorm -i 15 -p 3 -ref reference.tif target01.tif target02.tif target03.tif
ArrNorm was developing, designed and implemented by the Group of Forest and Carbon Monitoring System (SMByC), operated by the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) - Colombia.
Author and developer: Xavier C. Llano xavier.corredor.llano@gmail.com
Theoretical support, tester and product verification: SMByC-PDI group
ArrNorm is a free/libre software and is licensed under the GNU General Public License.