On Thu, Nov 21, 2013 at 10:20 PM, Michael Barton michael.bar...@asu.edu wrote:
I tried to implement this as an alternate way of histogram matching in
i.pansharpen (attached as i.pansharpen3). It works and gives different
colors, but it no longer sharpens. I’m attaching the code for you to look
I tried to implement this as an alternate way of histogram matching in
i.pansharpen (attached as i.pansharpen3). It works and gives different colors,
but it no longer sharpens. I’m attaching the code for you to look at (new
method matchhist_mean_sd). I’m tied up the rest of the afternoon. So
Because I am doing different pan sharpening algorithms, I think I got the equation backward previously. Now I am usingr.mapcalc "histmatched= ( pan - pan_mean ) / pan_sd *colorband_sd + colorband_meanwhere pan=high res panchromatic band and colorband=lower resolution restricted frequency bands
Nick Ves wrote:
The HPF algorithm about the histrogram matching states Stretch the
new multispectral image to match the mean and standard deviation of
the original (input) multispectral image
In that context why it is wrong to do:
Ouput - output/sddev(output)*sddev(input)
Output -
On 17/11/13 23:13, Nick Ves wrote:
The HPF algorithm about the histrogram matching states Stretch the
new multispectral image to match the mean and standard deviation of
the
original (input) multispectral image
In that context why it is wrong to do :
Ouput - output/sddev(output)*sddev(input)
Nick Ves wrote:
The HPF algorithm about the histrogram matching states Stretch the
new multispectral image to match the mean and standard deviation of
the original (input) multispectral image
In that context why it is wrong to do :
Ouput - output/sddev(output)*sddev(input)
Output -
On Nov 16, 2013, at 5:46 PM, Nikos Alexandris n...@nikosalexandris.net
wrote:
Replying to cmbarton:
If you have an image set that is more than 8bit, I can use it to test
some
things. i.histo.match is a nice module. But its objective is different
from
On Sun, Nov 17, 2013 at 8:47 PM, Michael Barton michael.bar...@asu.edu wrote:
I do not have an image set that is 8bit and floating point for testing. But
someone on the list did not too long ago.
This may be of interest:
http://landsat.usgs.gov/L8_12_bit.php
Landsat 8 products are delivered
The HPF algorithm about the histrogram matching states Stretch the
new multispectral image to match the mean and standard deviation of
the
original (input) multispectral image
In that context why it is wrong to do :
Ouput - output/sddev(output)*sddev(input)
Output - Output - mean(output) +
Attached is a new version of i.pansharpen (i.pansharpen2) to test.
I've changed it so that the user can set the number of grey levels (defaults to
256). One thing that worries me is this warning I got. It comes from r.stat:
WARNING: Raster map p034r032_7dp20010924_z13_80 is reading as integer
I took a look at the i.pansharpen code. The method matchhist does the histogram
matching. It creates cumulative distribution functions (CDF) of the source and
target histograms and then finds the closest values to match at each point on
the CDF. It is pretty thoroughly documented in the code.
On Sat, Nov 16, 2013 at 9:27 PM, Michael Barton michael.bar...@asu.edu wrote:
I took a look at the i.pansharpen code. The method matchhist does the
histogram matching. It creates cumulative distribution functions (CDF) of the
source and target histograms and then finds the closest values to
Michael Barton wrote:
I took a look at the i.pansharpen code. The method matchhist does the
histogram matching. It creates cumulative distribution functions (CDF) of
the source and target histograms and then finds the closest values to
match at each point on the CDF. It is pretty
On 14/11/13 02:25, Nikos Alexandris wrote:
Dear GRASS GIS users,
together with Nikos Ves, we share the i.fusion.hpf idea/proof of
concept. At the moment, we have a custom shell script named
`i.fusion.hpf` (an attempt for a proper GRASS add-on), which implements
the High Pass Filter Additive
Nikos Alexandris wrote:
together with Nikos Ves, we share the i.fusion.hpf idea/proof of
concept. At the moment, we have a custom shell script named
`i.fusion.hpf` (an attempt for a proper GRASS add-on), which implements
the High Pass Filter Additive (HPFA) Fusion Technique for
On 15/11/13 10:50, Nikos Alexandris wrote:
Nikos Alexandris wrote:
together with Nikos Ves, we share the i.fusion.hpf idea/proof of
concept. At the moment, we have a custom shell script named
`i.fusion.hpf` (an attempt for a proper GRASS add-on), which implements
the High Pass
Moritz Lennert wrote:
..
I just did a quick test:
pan in:
mean: 31.813
standard deviation: 3.75447
ms in:
mean: 15.2307
standard deviation: 3.55858
pan out:
mean: 15.6117
standard deviation: 3.23408
So for this example, mean seems to have been adjusted, but stddev not.
Thanks.
Nikos Alexandris wrote:
BTW, it works really great with QuickBird imagery.
For the matter,
- the Pan:
http://grasswiki.osgeo.org/wiki/File:Pan_04APR05050541-M2AS-00186011_01_P001.jpg
- the MS RGB:
http://grasswiki.osgeo.org/wiki/File:RGB_04APR05050541-M2AS-00186011_01_P001.jpg
-
On Fri, Nov 15, 2013 at 10:05 AM, Moritz Lennert
mlenn...@club.worldonline.be wrote:
On 14/11/13 02:25, Nikos Alexandris wrote:
Dear GRASS GIS users,
together with Nikos Ves, we share the i.fusion.hpf idea/proof of
concept. At the moment, we have a custom shell script named
`i.fusion.hpf`
I haven't had time today to look into this. I used a fairly standard histogram
matching algorithm, for which I cited the reference. But I don't remember
exactly how it went. Matching the SD was not part of it however.
Michael
__
C. Michael Barton
Director, Center
I agree that it would be best to have all pan sharpening algorithms together if
possible. New ones should be addable as new classes or methods in the existing
module. Note that I did employ parallel processing to the extent possible. It
might be possible to apply this kind of processing to
The script does not seem to work as posted. Working on it... (trying to
understand g.tempfile above all right now).
Nikos
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Here it goes, as Nikos Ves (depending on free time) works on the Python version
(and I'll try to
help), the bash proof of concept works. Attached here.
After some initial testing, we think that GRASS' results are just slightly more
smooth for when
applying the exact same parameters to get the
Dear GRASS GIS users,
together with Nikos Ves, we share the i.fusion.hpf idea/proof of concept. At
the moment,
we have a custom shell script named `i.fusion.hpf` (an attempt for a proper
GRASS add-on),
which implements the High Pass Filter Additive (HPFA) Fusion Technique for
Pan-Sharpening
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