Neteler: +cat

2013-05-21T14:51:41Z

+cat

← Older revision		Revision as of 14:51, 21 May 2013
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	[[Category: Documentation]]		[[Category: Documentation]]
			{{Python}}

⚠️MilenaN: added category

2010-08-02T23:17:23Z

added category

← Older revision		Revision as of 23:17, 2 August 2010
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	</source>		</source>

			[[Category: Documentation]]

⚠️Georg: /* Example */

2009-09-23T14:11:41Z

Example

← Older revision		Revision as of 14:11, 23 September 2009
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	Create groups using landsat7 data from the North Carolina dataset		Create groups using landsat7 data from the North Carolina dataset

	i.group lsat7_2002 in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,		i.group lsat7_2002 in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_61,lsat7_2002_62,lsat7_2002_70,lsat7_2002_80
	lsat7_2002_40,lsat7_2002_50,lsat7_2002_61,lsat7_2002_62,		i.group lsat7_2002 sub=lsat7_2002_multi in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_61,lsat7_2002_62,lsat7_2002_70
	lsat7_2002_70,lsat7_2002_80
	i.group lsat7_2002 sub=lsat7_2002_multi in=lsat7_2002_10,
	lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,
	lsat7_2002_61,lsat7_2002_62,lsat7_2002_70

	Create a signature file with 10 classes via cluster analysis		Create a signature file with 10 classes via cluster analysis

⚠️Georg: /* Example */

2009-09-23T13:47:57Z

Example

← Older revision		Revision as of 13:47, 23 September 2009
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	== Example ==		== Example ==

	Create groups using landsat7 data		Create groups using landsat7 data from the North Carolina dataset

	i.group lsat7_2002 in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,		i.group lsat7_2002 in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,

⚠️Georg at 13:46, 23 September 2009

2009-09-23T13:46:11Z

← Older revision		Revision as of 13:46, 23 September 2009
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	Probabilistic Label Relaxation is a post-classification algorithm. Is is based on the Bayes' theorem using conditional probabilities to further improve the results of a classification process that was carried out using class membership probabilities (for example maximum likelihood).		Probabilistic Label Relaxation is a post-classification algorithm. Is is based on the Bayes' theorem using conditional probabilities to further improve the results of a classification process that was carried out using class membership probabilities (for example maximum likelihood).

			See [http://www.file-upload.net/download-1894443/plr_in_grass.pdf.html Documentation] for details.

	== Example ==		== Example ==
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	=== Module output ===		=== Module output ===

			[[File:plr_drawing.png\|360px\|thumb\|center\|These results were created with landsat7 data from the North Carolina dataset by using a signature file of 10 classes, generated by i.cluster: a) i.maxlik b) i.plr.py, 4-neighbourhood, 1 iteration c) i.plr.py, 4-neighbourhood, 5 iterations d) i.plr.py, 8-neighbourhood, 1 iteration e) i.plr.py, 8-neighbourhood, 5 iterations]]

			== Known Issues ==

			* Very slow, code uses several loops, perfomance might be better when written in C
			* Probabilities are extracted by running i.maxlik for every class. This could be solved by implementing some changes into the i.maxlik module, so that probabilities used during the process of classification can be saved.
			* At this point the implementation of the ''getProbability(x,y)'' function is rather exemplary. The function returns ''1.0'' if the two given classes are identical and ''0.5'' in each other case. Even though this approach does show some effect in processing classification results, this is also the place to start optimization. One way to think of could be an automatically generated lookup-table, based on total numbers of pixels for each class. Of course a lookup-table could also be created by user input, but since ''N!'' entries would be needed for a classification using ''N'' classes, this would only be practicable for very small numbers of classes. A default value could be used to minimize the amount of user input to important entries.

⚠️Georg: Created page with '== Summary == Probabilistic Label Relaxation is a post-classification algorithm. Is is based on the Bayes' theorem using conditional probabilities to further improve the results…'

2009-09-23T13:21:09Z

Created page with '== Summary == Probabilistic Label Relaxation is a post-classification algorithm. Is is based on the Bayes' theorem using conditional probabilities to further improve the results…'

New page

== Summary ==

Probabilistic Label Relaxation is a post-classification algorithm. Is is based on the Bayes' theorem using conditional probabilities to further improve the results of a classification process that was carried out using class membership probabilities (for example maximum likelihood).

== Example ==

Create groups using landsat7 data

i.group lsat7_2002 in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,
lsat7_2002_40,lsat7_2002_50,lsat7_2002_61,lsat7_2002_62,
lsat7_2002_70,lsat7_2002_80
i.group lsat7_2002 sub=lsat7_2002_multi in=lsat7_2002_10,
lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,
lsat7_2002_61,lsat7_2002_62,lsat7_2002_70

Create a signature file with 10 classes via cluster analysis

i.cluster group=lsat7_2002 sub=lsat7_2002_multi sig=cluster_10 class=10

i.plr.py help page

Description:
Probabilistic label relaxation, postclassification filter

Usage:
i.plr.py group=string subgroup=string sigfile=string output=string
[iterations=value] [ntype=value] [--verbose] [--quiet]

Flags:
--v Verbose module output
--q Quiet module output

Parameters:
group image group to be used
subgroup image subgroup to be used
sigfile Path to sigfile
output Name for resulting raster file
iterations Number of iterations (1 by default)
ntype type of neighbourhood (4(default) or 8)

Example using 8-neighbourhood, 5 iterations

i.plr.py group=lsat_2002 sub=lsat7_2002_multi sig=cluster_10 out=lsat7_2002_plr it=5 nt=8

=== Module output ===

== Code ==

<source lang="python">
#!/usr/bin/env python
#
#############################################################################
#
# MODULE: i.plr.py
# AUTHOR(S): Georg Kaspar
# PURPOSE: Probabilistic label relaxation, postclassification filter
# COPYRIGHT: (C) 2009
#
# This program is free software under the GNU General Public
# License (>=v2). Read the file COPYING that comes with GRASS
# for details.
#
#############################################################################

#%Module
#% description: Probabilistic label relaxation, postclassification filter
#%End
#%option
#% key: group
#% type: string
#% description: image group to be used
#% required : yes
#%end
#%option
#% key: subgroup
#% type: string
#% description: image subgroup to be used
#% required : yes
#%end
#%option
#% key: sigfile
#% type: string
#% description: Path to sigfile
#% required : yes
#%end
#%option
#% key: output
#% type: string
#% description: Name for resulting raster file
#% required : yes
#%end
#%option
#% key: iterations
#% type: integer
#% description: Number of iterations (1 by default)
#% required : no
#%end
#%option
#% key: ntype
#% type: integer
#% description: type of neighbourhood (4(default) or 8)
#% required : no
#%end

import sys
import os
import numpy
import grass.script as grass
from osgeo import gdal, gdalnumeric, gdal_array
from osgeo.gdalconst import GDT_Byte

def getMetadata():
env = grass.gisenv()
global GISDBASE
global LOCATION_NAME
global MAPSET
GISDBASE = env['GISDBASE']
LOCATION_NAME = env['LOCATION_NAME']
MAPSET = env['MAPSET']

def splitSignatures(path, sigfile):
# split signature file into subfiles with 1 signature each
stream_in = open(path + sigfile, "r")
stream_in.next() # skip first line
counter = 0
stream_out = open(path + "plr_foo.sig", "w")
for line in stream_in:
if line[0] == "#":
stream_out.close()
counter += 1
stream_out = open(path + "plr_" + str(counter) + ".sig", "w")
stream_out.write("#produced by i.plr\n")
stream_out.write(line)
stream_out.close()
stream_in.close()
return counter

def normalizeProbabilities(counter):
arg = ""
for i in range(counter):
arg = arg + "+plr_rej_" + str(i)
arg = arg.strip('+')
print "calculating multiplicands, arg=" + arg
grass.run_command("r.mapcalc", multiplicand = "1./(" + arg + ")")
for i in range(counter):
print "normalizing probabilities for class " + str(i)
grass.run_command("r.mapcalc", plr_rej_norm = "plr_rej_" + str(i) + "*multiplicand")
grass.run_command("g.rename", rast="plr_rej_norm,plr_rej_norm_" + str(i))

def getProbability(a,b):
# TODO: Implement this!!!
if a == b:
return 1
else:
return 0.5

def cleanUp(path):
os.system("rm " + path + "/plr_*.*")
os.system("rm /tmp/plr_*.*")
grass.run_command("g.mremove", flags="f", quiet=True, rast="plr_*")

def plr_filter(probabilities, width, height, classes, type):
# create an empty n-dimesional array containing results
results = numpy.ones((classes,height,width))
print "starting label relaxation"
progress = 0
# for each pixel (except border)
for y in range(height):
p = int(float(y)/height * 100)
if p > progress:
print '\r' + str(p) + '%'
progress = p
for x in range(width):
# for all classes create neighbourhood and extract probabilities
for c in range(1, classes+1):
if (x == 0) or (x == width-1) or (y == 0) or (y == height-1):
results[c-1,y,x] = probabilities[c-1,y,x]
else:
if type == 8:
q = neighbourhoodFunction8(probabilities, x, y, c, classes)
else:
q = neighbourhoodFunction4(probabilities, x, y, c, classes)
p = probabilities[c-1, y, x]
# resulting cell contains the product of class probability and
# neighbourhood-function
results[c-1,y,x] = p * q
print ""
return results

def neighbourhoodFunction4(probabilities, x, y, z, classes):
n = []
neighbours = [[x-1,y],[x,y],[x+1,y],[x,y-1],[x,y+1]]
for i in neighbours:
l = []
# for each possible class
for c in range(1, classes+1):
l.append(getProbability(z, c) * float(probabilities[c-1,i[1],i[0]]))
n.append(sum(l))
return sum(n)

def neighbourhoodFunction8(probabilities, x, y, z, classes):
n = []
for j in range(y-1, y+2):
for i in range(x-1, x+2):
l = []
# for each possible class
for c in range(1, classes+1):
l.append(getProbability(z, c) * float(probabilities[c-1,j,i]))
n.append(sum(l))
return sum(n)

def createMap(probabilities, width, height, classes):
print "retrieving class labels"
results = numpy.ones((height,width))
progress = 0
for y in range(height):
p = int(float(y)/height * 100)
if p > progress:
print '\r' + str(p) + '%'
progress = p
for x in range(width):
max_class = 1
max_val = probabilities[0,y,x]
for c in range(2, classes+1):
current_val = probabilities[c-1,y,x]
if current_val > max_val:
max_val = current_val
max_class = c
results[y,x] = max_class
#results = probabilities.max(0)
return results;

def export(array, trans, proj):
driver = gdal.GetDriverByName('GTiff')
out = driver.Create('/tmp/plr_results.tif', array.shape[1], array.shape[0], 1, GDT_Byte)
out.SetGeoTransform(trans)
out.SetProjection(proj)
gdal_array.BandWriteArray(out.GetRasterBand(1), array)

def main():
# fetch parameters
group = options['group']
subgroup = options['subgroup']
sigfile = options['sigfile']
output = options['output']
iterations = options['iterations']
ntype = options['ntype']

if iterations == "":
iterations = 1
iterations = int(iterations)

if ntype == "":
ntype = 4
ntype = int(ntype)

# fetch Metadata
getMetadata()

# split sigfiles
sigpath = GISDBASE + "/" + LOCATION_NAME + "/" + MAPSET + "/group/" + group + "/subgroup/" + subgroup + "/sig/"
counter = splitSignatures(sigpath, sigfile)
print "found " + str(counter) + " signatures"

l = []
for i in range(1, counter+1):
# extract probabilities
print "extracting probabilities for class " + str(i)
grass.run_command("i.maxlik", group=group, subgroup=subgroup, sigfile="plr_" + str(i) + ".sig", clas="plr_class" + str(i), reject="plr_rej_" + str(i))
# export from GRASS
print "exporting probabilities for class " + str(i) + " to /tmp"
grass.run_command("r.out.gdal", inp="plr_rej_" + str(i), out="/tmp/plr_rej_" + str(i) + ".tif")

# import via gdal
print "reading file"
tif = gdal.Open("/tmp/plr_rej_" + str(i) + ".tif")
l.append(tif.ReadAsArray())

if i == 1:
width = l[0].shape[1]
height = l[0].shape[0]
trans = tif.GetGeoTransform()
proj = tif.GetProjection()

# create n-dimensional array
print "creating 3D-array"
probabilities = numpy.array(l)

print "Image size: " + str(width) + "x" + str(height)
print "using " + str(ntype) + "-neighbourhood"
# invoke relaxation process
results = probabilities.copy()
for i in range(int(iterations)):
print str(int(iterations)-i) + " iteration(s) to go..."
results = plr_filter(results, width, height, counter, ntype)
labels = createMap(results, width, height, counter)

# exporting results
print "exporting results to /tmp"
export(labels, trans, proj)

# import via gdal into GRASS
print "reading results"
grass.run_command("r.in.gdal", inp="/tmp/plr_results.tif", out=output)
grass.run_command("r.colors", map=output, color="rainbow")

# clean up
print "removing temporary files"
cleanUp(sigpath)

if __name__ == "__main__":
options, flags = grass.parser()
main()

</source>

I.plr.py - Revision history

Neteler: +cat

⚠️MilenaN: added category

⚠️Georg: /* Example */

⚠️Georg: /* Example */

⚠️Georg at 13:46, 23 September 2009

⚠️Georg: Created page with '== Summary == Probabilistic Label Relaxation is a post-classification algorithm. Is is based on the Bayes' theorem using conditional probabilities to further improve the results…'