Hey!
All right :-). Try what I posted on [2015-05-04 22:14:41]:
# if Reflectance 1, set to 1000, else round and multiply by 1000
r.mapcalc --o TopoCorr_B_Trimmed_DOS1_ToCR_${Band_No}_${SCENE} = if(
TopoCorr.B.Trimmed.DOS1.ToCR.${Band_No}@${SCENE} 1, 1000, round( 1000
*
* joanna mardas joanna.mar...@wp.pl [2015-05-10 02:28:02 +0200]:
Hey!
joanna, once again, the easy other way is posted in my first reply, I
think. You just need to multiply with 1000, perform the histogram
matching, then divide by 1000.0 to get back to floats.
So I did that:
On 07/05/15 20:06, Nikos Alexandris wrote:
Moritz:
AFAIU, you could use the vibility in km instead of the aerosol optical
depth. i.atcorr will then calculate optical depth based on a standard
model. To get visibility find a meteorological station close to your
image and get the info for the
Nikos:
If you go all the way from DN to Top-of-Atmosphere reflectances (via
i.landsat.toar), then to Top-of-Canopy (via i.atcorr), you'll have
floating point values ranging in [0, 1.0]. If you recode this back to
8-bit, you should consider whether there is an important loss of
Thanks Nikos!
I'm not sure that for Landsat 5 the loss is so important, but you can
visually compare an image recoded to 0-255 with the one coming out of
i.landsat.toar...
Nor am I sure about it. Landsat5 is 8-bit. But one should definitively
consider it, and mention the
decisions
Hello! It's me again :)
If you go all the way from DN to Top-of-Atmosphere reflectances (via
i.landsat.toar), then to Top-of-Canopy (via i.atcorr), you'll have
floating point values ranging in [0, 1.0]. If you recode this back to
8-bit,
you should consider whether there is an important loss
* joanna mardas joanna.mar...@wp.pl [2015-05-05 00:19:02 +0200]:
Hello!
You both are my heros! Thank you Moritz and Nikos!
r.recode worked :D I did i.histo.match on two test images and it looks
fine.
If you go all the way from DN to Top-of-Atmosphere reflectances (via
Hello,
I'm totally new user of GRASS and of course I have some problems. I want to do the pre-processing of Landsat (5TM) images (for further band composite, classification and NDVI) and I was using the tips from http://grasswiki.osgeo.org/wiki/LANDSAT#Pre-Processing I did the i.landsat.toar I
On 04/05/15 11:28, joanna mardas wrote:
Hello,
I'm totally new user of GRASS and of course I have some problems. I want
to do the pre-processing of Landsat (5TM) images (for further band
composite, classification and NDVI) and I was using the tips from
joanna mardas wrote:
Hello,
Hi Joanna,
I'm totally new user of GRASS and of course I have some problems. I want
to do the pre-processing of Landsat (5TM) images (for further band
composite, classification and NDVI) and I was using the tips from
joanna mardas wrote:
I have fragments of original landsat images
imported to GRASS and i.histo.match works with them without any
problems.
Nikos Alexandris :
That is so because i.histo.match, as Moritz notes below, support for
8-bit raster data.
Correction: support is there for
Hello!
You both are my heros! Thank you Moritz and Nikos!
r.recode worked :D I did i.histo.match on two test images and it looks fine. So now the rest of bands, and then band composite and supervised classification (I've found nice tutorial on youtube, so I'm gonna follow it, or I will try
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