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+#!/usr/bin/env python
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+
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+############################################################################
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+#
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+# MODULE: i.pansharpen
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+#
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+# AUTHOR(S): Overall script by Michael Barton (ASU)
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+# Brovey transformation in i.fusion.brovey by Markus Neteler <<neteler at osgeo org>>
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+# i.fusion brovey converted to Python by Glynn Clements
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+# IHS and PCA transformation added by Michael Barton (ASU)
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+# histogram matching algorithm by Michael Barton and Luca Delucchi, Fondazione E. Mach (Italy)
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+# Thanks to Markus Metz for help with PCA inversion
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+# Thanks to Hamish Bowman for parallel processing algorithm
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+#
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+# PURPOSE: Sharpening of 3 RGB channels using a high-resolution panchromatic channel
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+#
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+# COPYRIGHT: (C) 2002-2012 by the GRASS Development Team
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+#
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+# This program is free software under the GNU General Public
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+# License (>=v2). Read the file COPYING that comes with GRASS
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+# for details.
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+#
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+# REFERENCES:
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+# Roller, N.E.G. and Cox, S., 1980. Comparison of Landsat MSS and merged MSS/RBV
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+# data for analysis of natural vegetation. Proc. of the 14th International
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+# Symposium on Remote Sensing of Environment, San Jose, Costa Rica, 23-30 April, pp. 1001-1007.
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+#
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+# Amarsaikhan, D., & Douglas, T. (2004). Data fusion and multisource image classification.
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+# International Journal of Remote Sensing, 25(17), 3529-3539.
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+#
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+# Behnia, P. (2005). Comparison between four methods for data fusion of ETM+
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+# multispectral and pan images. Geo-spatial Information Science, 8(2), 98-103
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+#
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+# for LANDSAT 5: see Pohl, C 1996 and others
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+#
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+#############################################################################
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+
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+#%Module
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+#% description: Image fusion algorithms to sharpen multispectral with high-res panchromatic channels
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+#% keywords: imagery
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+#% keywords: fusion
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+#% keywords: sharpen
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+#% keywords: Brovey
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+#% keywords: IHS
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+#% keywords: PCA
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+#% overwrite: yes
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+#%End
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+#%option
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+#% key: sharpen
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+#% description: Choose pan sharpening method
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+#% options: brovey,ihs,pca
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+#% answer: ihs
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+#% required: yes
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+#%end
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+#%option G_OPT_R_INPUT
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+#% key: ms3
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+#% description: Input raster map for red channel
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+#%end
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+#%option G_OPT_R_INPUT
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+#% key: ms2
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+#% description: Input raster map for green channel
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+#%end
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+#%option G_OPT_R_INPUT
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+#% key: ms1
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+#% description: Input raster map for blue channel
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+#%end
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+#% option G_OPT_R_INPUT
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+#% key: pan
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+#% description: Input raster map for high resolution panchromatic channel
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+#%end
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+#%option
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+#% key: output_prefix
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+#% type: string
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+#% description: Prefix for output raster maps
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+#% required : yes
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+#%end
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+#%flag
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+#% key: s
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+#% description: Serial processing rather than parallel processing
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+#%end
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+#%flag
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+#% key: l
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+#% description: Rebalance blue channel for landsat maps
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+#%end
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+
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+import sys
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+import os
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+import numpy as np
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+import grass.script as grass
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+
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+def main():
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+ sharpen = options['sharpen'] # sharpening algorithm
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+ ms1 = options['ms1'] # blue channel
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+ ms2 = options['ms2'] # green channel
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+ ms3 = options['ms3'] # red channel
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+ pan = options['pan'] # high res pan channel
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+ out = options['output_prefix'] # prefix for output RGB maps
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+ bladjust = flags['l'] # adjust blue channel
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+ sproc = flags['s'] # serial processing
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+
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+ outb = grass.core.find_file('%s_blue' % out)
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+ outg = grass.core.find_file('%s_green' % out)
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+ outr = grass.core.find_file('%s_red' % out)
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+
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+ if (outb['name'] != '' or outg['name'] != '' or outr['name'] != '') and \
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+ (not grass.overwrite() and not flags['o']):
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+ grass.warning(_('Maps with selected output prefix names already exist. \
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+ Delete them or use overwrite flag'))
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+ return
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+
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+ pid = str(os.getpid())
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+
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+ #get PAN resolution:
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+ kv = grass.raster_info(map = pan)
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+ nsres = kv['nsres']
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+ ewres = kv['ewres']
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+ panres = (nsres + ewres) / 2
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+
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+ # clone current region
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+ grass.use_temp_region()
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+
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+ grass.run_command('g.region', res = panres, align = pan)
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+
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+ grass.message('\n ')
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+ grass.message(_("Performing pan sharpening with hi res pan image: %f" % panres))
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+
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+ if sharpen == "brovey":
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+ grass.message(_("Using Brovey algorithm"))
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+
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+ #pan/intensity histogram matching using linear regression
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+ outname = 'tmp%s_pan1' % pid
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+ panmatch1 = matchhist(pan, ms1, outname)
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+
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+ outname = 'tmp%s_pan2' % pid
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+ panmatch2 = matchhist(pan, ms2, outname)
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+
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+ outname = 'tmp%s_pan3' % pid
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+ panmatch3 = matchhist(pan, ms3, outname)
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+
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+ outr = '%s_red' % out
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+ outg = '%s_green' % out
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+ outb = '%s_blue' % out
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+
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+ #calculate brovey transformation
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+ grass.message('\n ')
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+ grass.message(_("Calculating Brovey transformation..."))
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+
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+ if sproc:
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+ # serial processing
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+ e = '''eval(k = "$ms1" + "$ms2" + "$ms3")
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+ "$outr" = "$ms3" * "$panmatch3" / k
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+ "$outg" = "$ms2" * "$panmatch2" / k
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+ "$outb" = "$ms1" * "$panmatch1" / k'''
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+ grass.mapcalc(e, outr=outr, outg=outg, outb=outb,
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+ panmatch1=panmatch1, panmatch2=panmatch2, panmatch3=panmatch3,
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+ ms1=ms1, ms2=ms2, ms3=ms3, overwrite=True)
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+ else:
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+ # parallel processing
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+ pb = grass.mapcalc_start('%s_blue = (%s * %s) / (%s + %s + %s)' %
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+ (out, ms1, panmatch1, ms1, ms2, ms3),
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+ overwrite=True)
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+ pg = grass.mapcalc_start('%s_green = (%s * %s) / (%s + %s + %s)' %
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+ (out, ms2, panmatch2, ms1, ms2, ms3),
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+ overwrite=True)
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+ pr = grass.mapcalc_start('%s_red = (%s * %s) / (%s + %s + %s)' %
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+ (out, ms3, panmatch3, ms1, ms2, ms3),
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+ overwrite=True)
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+
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+ pb.wait()
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+ pg.wait()
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+ pr.wait()
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+
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+
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+ # Cleanup
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+ grass.run_command('g.remove', quiet=True, rast='%s,%s,%s' % (panmatch1, panmatch2, panmatch3))
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+
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+ elif sharpen == "ihs":
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+ grass.message(_("Using IHS<->RGB algorithm"))
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+ #transform RGB channels into IHS color space
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+ grass.message('\n ')
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+ grass.message(_("Transforming to IHS color space..."))
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+ grass.run_command('i.rgb.his', overwrite=True,
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+ red_input=ms3,
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+ green_input=ms2,
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+ blue_input=ms1,
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+ hue_output="tmp%s_hue" % pid,
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+ intensity_output="tmp%s_int" % pid,
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+ saturation_output="tmp%s_sat" % pid)
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+
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+ #pan/intensity histogram matching using linear regression
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+ target = "tmp%s_int" % pid
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+ outname = "tmp%s_pan_int" % pid
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+ panmatch = matchhist(pan, target, outname)
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+
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+ #substitute pan for intensity channel and transform back to RGB color space
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+ grass.message('\n ')
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+ grass.message(_("Transforming back to RGB color space and sharpening..."))
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+ grass.run_command('i.his.rgb', overwrite=True,
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+ hue_input="tmp%s_hue" % pid,
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+ intensity_input="%s" % panmatch,
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+ saturation_input="tmp%s_sat" % pid,
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+ red_output="%s_red" % out,
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+ green_output="%s_green" % out,
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+ blue_output="%s_blue" % out)
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+
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+ # Cleanup
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+ grass.run_command('g.remove', quiet=True, rast=panmatch)
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+
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+ elif sharpen == "pca":
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+ grass.message(_("Using PCA/inverse PCA algorithm"))
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+ grass.message('\n ')
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+ grass.message(_("Creating PCA images and calculating eigenvectors..."))
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+
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+ #initial PCA with RGB channels
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+ pca_out = grass.read_command('i.pca', quiet=True, rescale='0,0', input='%s,%s,%s' % (ms1, ms2, ms3), output_prefix='tmp%s.pca' % pid)
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+ b1evect = []
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+ b2evect = []
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+ b3evect = []
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+ for l in pca_out.replace('(',',').replace(')',',').splitlines():
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+ b1evect.append(float(l.split(',')[1]))
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+ b2evect.append(float(l.split(',')[2]))
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+ b3evect.append(float(l.split(',')[3]))
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+
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+ #inverse PCA with hi res pan channel substituted for principal component 1
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+ pca1 = 'tmp%s.pca.1' % pid
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+ pca2 = 'tmp%s.pca.2' % pid
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+ pca3 = 'tmp%s.pca.3' % pid
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+ b1evect1 = b1evect[0]
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+ b1evect2 = b1evect[1]
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+ b1evect3 = b1evect[2]
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+ b2evect1 = b2evect[0]
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+ b2evect2 = b2evect[1]
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+ b2evect3 = b2evect[2]
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+ b3evect1 = b3evect[0]
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+ b3evect2 = b3evect[1]
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+ b3evect3 = b3evect[2]
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+
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+ outname = 'tmp%s_pan' % pid
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+ panmatch = matchhist(pan, ms1, outname)
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+
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+ grass.message('\n ')
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+ grass.message(_("Performing inverse PCA ..."))
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+
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+ stats1 = grass.parse_command("r.univar", map=ms1, flags='g',
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+ parse=(grass.parse_key_val, { 'sep' : '=' }))
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+ stats2 = grass.parse_command("r.univar", map=ms2, flags='g',
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+ parse=(grass.parse_key_val, { 'sep' : '=' }))
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+ stats3 = grass.parse_command("r.univar", map=ms3, flags='g',
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+ parse=(grass.parse_key_val, { 'sep' : '=' }))
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+
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+ b1mean = float(stats1['mean'])
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+ b2mean = float(stats2['mean'])
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+ b3mean = float(stats3['mean'])
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+
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+
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+ if sproc:
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+ # serial processing
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+ e = '''eval(k = "$ms1" + "$ms2" + "$ms3")
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+ "$outr" = "$ms3" * "$panmatch3" / k
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+ "$outg" = "$ms2" * "$panmatch2" / k
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+ "$outb" = "$ms1" * "$panmatch1" / k'''
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+
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+ outr = '%s_red' % out
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+ outg = '%s_green' % out
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+ outb = '%s_blue' % out
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+
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+ cmd1 = "$outb = ($panmatch * $b1evect1) + ($pca2 * $b2evect1) + ($pca3 * $b3evect1) + $b1mean"
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+ cmd2 = "$outg = ($panmatch * $b1evect2) + ($pca2 * $b2evect1) + ($pca3 * $b3evect2) + $b2mean"
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+ cmd3 = "$outr = ($panmatch * $b1evect3) + ($pca2 * $b2evect3) + ($pca3 * $b3evect3) + $b3mean"
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+
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+ cmd = '\n'.join([cmd1, cmd2, cmd3])
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+
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+ grass.mapcalc(cmd, outb=outb, outg=outg, outr=outr,
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+ panmatch=panmatch, pca2=pca2, pca3=pca3,
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+ b1evect1=b1evect1, b2evect1=b2evect1, b3evect1=b3evect1,
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+ b1evect2=b1evect2, b2evect2=b2evect2, b3evect2=b3evect2,
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+ b1evect3=b1evect3, b2evect3=b2evect3, b3evect3=b3evect3,
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+ b1mean=b1mean, b2mean=b2mean, b3mean=b3mean,
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+ overwrite=True)
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+ else:
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+ # parallel processing
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+ pb = grass.mapcalc_start('%s_blue = (%s * %f) + (%s * %f) + (%s * %f) + %f'
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+ % (out, panmatch, b1evect1, pca2, b2evect1, pca3, b3evect1, b1mean),
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+ overwrite=True)
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+
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+ pg = grass.mapcalc_start('%s_green = (%s * %f) + (%s * %f) + (%s * %f) + %f'
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+ % (out, panmatch, b1evect2, pca2, b2evect2, pca3, b3evect2, b2mean),
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+ overwrite=True)
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+
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+ pr = grass.mapcalc_start('%s_red = (%s * %f) + (%s * %f) + (%s * %f) + %f'
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+ % (out, panmatch, b1evect3, pca2, b2evect3, pca3, b3evect3, b3mean),
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+ overwrite=True)
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+
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+ pr.wait()
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+ pg.wait()
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+ pb.wait()
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+
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+
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+ # Cleanup
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+ grass.run_command('g.mremove', flags='f', quiet=True, rast='tmp%s*,%s' % (pid,panmatch))
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+
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+ #Could add other sharpening algorithms here, e.g. wavelet transformation
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+
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+ grass.message('\n ')
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+ grass.message(_("Assigning grey equalized color tables to output images..."))
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+ #equalized grey scales give best contrast
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+ for ch in ['red', 'green', 'blue']:
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+ grass.run_command('r.colors', quiet=True, map = "%s_%s" % (out, ch), flags="e", col = 'grey')
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+
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+ #Landsat too blue-ish because panchromatic band less sensitive to blue light,
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+ # so output blue channed can be modified
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+ if bladjust:
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+ grass.message('\n ')
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+ grass.message(_("Adjusting blue channel color table..."))
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+ rules = grass.tempfile()
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+ colors = open(rules, 'w')
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+ colors.write('5 0 0 0\n20 200 200 200\n40 230 230 230\n67 255 255 255 \n')
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+ colors.close()
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+
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+ grass.run_command('r.colors', map="%s_blue" % out, rules=rules)
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+ os.remove(rules)
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+
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+ #output notice
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+ grass.message('\n ')
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+ grass.message(_("The following pan-sharpened output maps have been generated:"))
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+ for ch in ['red', 'green', 'blue']:
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+ grass.message(_("%s_%s") % (out, ch))
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+
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+ grass.message('\n ')
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+ grass.message(_("To visualize output, run: g.region -p rast=%s.red" % out))
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+ grass.message(_("d.rgb r=%s_red g=%s_green b=%s_blue" % (out, out, out)))
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+ grass.message('\n ')
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+ grass.message(_("If desired, combine channels into a single RGB map with 'r.composite'."))
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+ grass.message(_("Channel colors can be rebalanced using i.landsat.rgb."))
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+
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+ # write cmd history:
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+ for ch in ['red', 'green', 'blue']:
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+ grass.raster_history("%s_%s" % (out, ch))
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+
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+ # Cleanup
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+ grass.run_command('g.mremove', flags="f", rast="tmp%s*" % pid, quiet=True)
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+
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+
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+def matchhist(original, target, matched):
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+ #pan/intensity histogram matching using numpy arrays
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+ grass.message('\n ')
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+ grass.message(_("Histogram matching..."))
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+
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+ # input images
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+ original = original.split('@')[0]
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+ target = target.split('@')[0]
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+ images = [original, target]
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+
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+ # create a dictionary to hold arrays for each image
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+ arrays = {}
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+
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+ for i in images:
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+ # calculate number of cells for each grey value for for each image
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+ stats_out = grass.pipe_command('r.stats', flags='cin', input= i, fs=':')
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+ stats = stats_out.communicate()[0].split('\n')[:-1]
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+ stats_dict = dict( s.split(':', 1) for s in stats)
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+ total_cells = 0 # total non-null cells
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+ for j in stats_dict:
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+ stats_dict[j] = int(stats_dict[j])
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+ if j != '*':
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+ total_cells += stats_dict[j]
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+
|
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+ # Make a 2x256 structured array for each image with a
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|
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+ # cumulative distribution function (CDF) for each grey value.
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+ # Grey value is the integer (i4) and cdf is float (f4).
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+
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+ arrays[i] = np.zeros((256,),dtype=('i4,f4'))
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+ cum_cells = 0 # cumulative total of cells for sum of current and all lower grey values
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+
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+ for n in range(0,256):
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+ if str(n) in stats_dict:
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+ num_cells = stats_dict[str(n)]
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+ else:
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+ num_cells = 0
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|
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+
|
|
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+ cum_cells += num_cells
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|
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+
|
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+ # cdf is the the number of cells at or below a given grey value
|
|
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+ # divided by the total number of cells
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|
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+ cdf = float(cum_cells) / float(total_cells)
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|
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+
|
|
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+ # insert values into array
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|
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+ arrays[i][n] = (n, cdf)
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|
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+
|
|
|
+ # open file for reclass rules
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|
|
+ outfile = open(grass.tempfile(), 'w')
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|
|
+ new_grey = 0
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|
|
+
|
|
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+ for i in arrays[original]:
|
|
|
+ # for each grey value and corresponding cdf value in original, find the
|
|
|
+ # cdf value in target that is closest to the target cdf value
|
|
|
+ difference_list = []
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|
|
+ for j in arrays[target]:
|
|
|
+ # make a list of the difference between each original cdf value and
|
|
|
+ # the target cdf value
|
|
|
+ difference_list.append(abs(i[1] - j[1]))
|
|
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+
|
|
|
+ # get the smallest difference in the list
|
|
|
+ min_difference = min(difference_list)
|
|
|
+
|
|
|
+ for j in arrays[target]:
|
|
|
+ # find the grey value in target that correspondes to the cdf
|
|
|
+ # closest to the original cdf
|
|
|
+ if j[1] == i[1] + min_difference or j[1] == i[1] - min_difference:
|
|
|
+ # build a reclass rules file from the original grey value and
|
|
|
+ # corresponding grey value from target
|
|
|
+ out_line = "%d = %d\n" % (i[0], j[0])
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|
|
+ outfile.write(out_line)
|
|
|
+ break
|
|
|
+
|
|
|
+ outfile.close()
|
|
|
+
|
|
|
+ # create reclass of target from reclass rules file
|
|
|
+ result = grass.core.find_file(matched, element = 'cell')
|
|
|
+ if result['fullname']:
|
|
|
+ grass.run_command('g.remove', quiet=True, rast=matched)
|
|
|
+ grass.run_command('r.reclass', input=original, out=matched, rules=outfile.name)
|
|
|
+ else:
|
|
|
+ grass.run_command('r.reclass', input=original, out=matched, rules=outfile.name)
|
|
|
+
|
|
|
+ # Cleanup
|
|
|
+ # remove the rules file
|
|
|
+ grass.try_remove(outfile.name)
|
|
|
+
|
|
|
+ # return reclass of target with histogram that matches original
|
|
|
+ return matched
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ options, flags = grass.parser()
|
|
|
+ main()
|
Michael Barton
