Greetings
I have created a script to detect clouds/shadows (i.landsat.acca-
Great work Jorge), grow of these pixels and then identify an area that
is common to all bands (7).And at the end, turn these pixels into
NULL. Above this paragraph I placed a part of this code. My problem is
that, for regular satellite images it's a very slow process. How can
I optimize it? any suggestion?
Thanks
def cloud_detection(output_t,output_thermal_t,landsatsensor):
#Create temporary map for cloud and shadows
tempC= "myscript.tmpcloud"
#Create temporary map for valid areas
tempV= "myscript.tmpvalid"
#Create temporary file
tempB= "myscript.tmpB"
#Due to a shift between it will be necessary to identify a valid area
for all bands
try:
grass.mapcalc("$tempV= if(($B1>=0 && $B7>=0 && $B3>=0 &&
$B4>=0 && $B5>=0 && $B7>=0 &&
$B6>=0),1,null())",tempV=tempV,B1=output_t[0],B2=output_t[1],B3=output_t[2],B4=output_t[3],B5=output_t[4],B7=output_t[4],B6=output_thermal_t)
except:
grass.fatal(_("ERROR"))
#Apply i.landsat.acca module according to landsat sensor type
if landsatsensor == '5TM':
p=grass.run_command("i.landsat.acca", band_prefix="B",
output=tempC, flags="52s")
elif landsatsensor == '7ETM':
p=grass.run_command("i.landsat.acca", band_prefix="B",
output=tempC,flags="2s")
#Apply r.grow to detected clouds and shadows
p=grass.run_command("r.grow", input=tempC, output=tempC,
overwrite=True, radius=3, old=1, new=1, metric="euclidean")
p=grass.run_command("r.null", map=tempC, null=0)
p=grass.run_command("r.null", map=tempV, null=0)
#Cross bands (output_t) with r.grow ou*tput and Valid area
for i in output_t:
try:
grass.mapcalc("$output= $input",output=tempB, input=i)
except:
grass.fatal(_("ERROR"))
try:
grass.mapcalc("$output= if(($valid==1 &&
$cloud!=1),$input,null())",output=i, input=tempB,
valid=tempV,cloud=tempC)
except:
grass.fatal(_("ERROR"))
#Eliminate temporary raster
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