Jukka, (and others)

Thank you for your reply.  I think I figured out how lut works.  If you don't 
mind please check and make sure I have this correct:

I can use ArcGIS to report the correct input and output values for a n=2.5 
standard deviation by going to layers->symbology->stretch->histograms->Info 
window.  Once I have those values I can modify my map file to include for 
example:

PROCESSING "LUT_1=73:0,226:255" #changes red band input of 0-73 to 0, inputs of 
226-255 to 255, inputs between 73-226 linearly interpolated.
PROCESSING "LUT_2=60:0,199:255" #changes green band input of 0-60 to 0, inputs 
of 199-255 to 255, inputs between 60-199 linearly interpolated.
PROCESSING "LUT_3=35:0,155:255" #changes blue band input of 0-35 to 0, inputs 
of 155-255 to 255, inputs between 35-155 linearly interpolated.

P.S.
Before I knew what the heck I was doing with LUT I noticed that I could 
identify wet areas, and tree cover quite easily with the following combinations:

PROCESSING "LUT=50:50,60:250,70:250" is a good way to identify tree cover.
PROCESSING "LUT=50:50,60:250,70:70"  is a good way to identify wet areas.

Thank You
Sincerely,
Mark Volz, GISP


From: Rahkonen Jukka (MML) [mailto:[email protected]]
Sent: Monday, May 25, 2015 9:18 AM
To: Mark Volz; [email protected]
Subject: Re: stretching imagery in MapServer

Hi,


As far as I know automatic stretch by standard deviation is not supported. You 
can do automatic min-max stretch with PROCESSING "SCALE=AUTO". If your source 
data is uniform you may reach a good result with manual scale settings or with 
PROCESSING "LUT=  "



-Jukka Rahkonen-


Mark Volz wrote:

Hello,

I added our new Pictometry Air Photos on our mapping site at 
http://geomoose.lyonco.org/geomoose2/geomoose.html.  The air photos seem 
somewhat washed out.  In ArcGIS I was able to get a nicer looking image by 
stretching the air photo using standard deviations.  Can we stretch imagery in 
MapServer?  If not please let me know if you have a comment regarding how I can 
improve the image quality.

Thanks
Sincerely,
Mark Volz, GISP


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