Use the overlap region to pick PIFs (pseudoinvariant features), run a regression between the band values of the PIFs between one image and the other, apply the regression coefficients, and voila! If you just assume the entire overlap region is fine (no clouds or changed pixels), and they are orthorectified to one another, then you can extract the entire overlap region and run the regression on all those pixels.
The other way is to calculate the mean and stdev of the band values, and adjust them that way, but I always felt the regression approach is a little more straightforward.
There's a few automated algorithms out there to do this -- anyone want to write some code for it 
?
http://scholar.google.com/scholar?sourceid=Mozilla-search&q=radiometric+normalization+"remote+sensing"
Canty's got some IDL code to do radiometric normalization that can be downloaded -- IDL's an easy language to read, so the code should be easy to port to something GRASS could use: http://www.crcpress.com/e_products/downloads/download.asp?cat_no=7251
--j
Michael Barton wrote:
On 5/20/08 8:24 PM, "grass-user-request@lists.osgeo.org"
<grass-user-request@lists.osgeo.org> wrote:
Date: Tue, 20 May 2008 11:44:51 -0700
From: Jonathan Greenberg <greenberg@ucdavis.edu>
Subject: Re: [GRASS-user] value differences between landsat images
To: Juan Manuel Barreneche <jumanbar@gmail.com>
Cc: grass-user@lists.osgeo.org
Message-ID: <48331C23.8070705@ucdavis.edu>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Juan:
It just means your images aren't radiometrically normalized to one
another. If you correctly normalize them you won't see a line in the
mosaic.
I'll bite. How do you radiometrically normalize adjacent Landsat images. I'd
love to get rid of the lines.
Michael
Hamish
JM
-------------------------------------------------------
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Michael Barton, Professor of Anthropology
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