BrightnessNormalization.py

#! /usr/bin/env python

########################################################################################################################
#
# BrightnessNormalization.py
#
# A python script to perform Brigtness Normalization of hyperspectral data
#
# Info: The script apply the Brightness Normalization presented in
#       Feilhauer et al., 2010 to all rasters contained in a folder
#
# Author: Javier Lopatin
# Email: javierlopatin@gmail.com
# Last changes: 07/12/2016
# Version: 1.0
#
# example: python BrightnessNormalization.py -i raster.tif
#
# Bibliography:
#
# Feilhauer, H., Asner, G. P., Martin, R. E., Schmidtlein, S. (2010): Brightness-normalized Partial Least Squares
# Regression for hyperspectral data. Journal of Quantitative Spectroscopy and Radiative Transfer 111(12-13),
# pp. 1947–1957. 10.1016/j.jqsrt.2010.03.007
#
########################################################################################################################

from __future__ import division
import os, glob, argparse
import numpy as np
try:
   import rasterio
except ImportError:
   print("ERROR: Could not import Rasterio Python library.")
   print("Check if Rasterio is installed.")

## Functions

def BrigthnessNormalization(img):
    r = img / np.sqrt( np.sum((img**2), 0) )
    return r

def saveImage(img, inputRaster):
    # Save TIF image to a nre directory of name MNF
    output = name[:-4] + "_BN.tif"
    new_dataset = rasterio.open(output, 'w', driver='GTiff',
               height=inputRaster.shape[0], width=inputRaster.shape[1],
               count=int(img.shape[0]), dtype=str(img.dtype),
               crs=inputRaster.crs, transform=inputRaster.transform)
    new_dataset.write(img)
    new_dataset.close()

### Run process

if __name__ == "__main__":

   # create the arguments for the algorithm
   parser = argparse.ArgumentParser()

   parser.add_argument('-i','--input', help='Imput raster', type=str, required=True)
   parser.add_argument('--version', action='version', version='%(prog)s 1.0')
   args = vars(parser.parse_args())

   # input raster
   image = args["input"]

   name = os.path.basename(image)
   r = rasterio.open(image)
   img = r.read()
   print("Normalizing "+name)
   bn = np.apply_along_axis(BrigthnessNormalization, 0, img)
   saveImage(bn, r)