Nikos, What about just using r.rescale to rescale this?
Michael ____________________ C. Michael Barton Director, Center for Social Dynamics & Complexity Professor of Anthropology, School of Human Evolution & Social Change Arizona State University voice: 480-965-6262 (SHESC), 480-965-8130/727-9746 (CSDC) fax: 480-965-7671 (SHESC), 480-727-0709 (CSDC) www: http://www.public.asu.edu/~cmbarton, http://csdc.asu.edu On Jul 31, 2013, at 8:07 PM, <[email protected]<mailto:[email protected]>> wrote: From: Nikos Alexandris <[email protected]<mailto:[email protected]>> Subject: Re: [GRASS-dev] On i.histo.match (Re: On (Landsat) imagery naming patterns) Date: July 31, 2013 3:38:24 PM MST To: Hamish <[email protected]<mailto:[email protected]>> Cc: <[email protected]<mailto:[email protected]>>, Markus Metz <[email protected]<mailto:[email protected]>> Nikos wrote: Just FYI, results look nice! I even convert back to 0-1.0 via r.mapcalc --o "${HistoMatchedMap} = ${HistoMatchedMap} / 1000.0" ps- I wonder if *10000 is *better* for higher precision? By the way, that was Landsat5! depending on the sensor's 8-bitnesss or not, you can probably calculate by hand now many significant digits are useful. A little bit extra probably doesn't hurt. 1 / 2^8 = 0.00390625 I didn't pay too much attention back then as I was under enormous time pressure. But, I think I didn't miss much of the precision with respect to the final product's scope. Except if the last 4 (or 3, can't remember) digits make up a great deal when histo-matching. Back to present. QuickBird is an 11-bit sensor [1] and data are delivered as either 8-bit or 16-bit. In this case we have 1 / 2^11 = 0.0004882812 Does that say anything about the significant digits? I want to rescale QuickBird bands in order to use'em with i.pansharpen. I am thinking to go from Reflectance (double precision) to integer (as above, only this time I'd multiply with a number as big as it takes to keep all decimals) and then rescale to [0, 255]. Makes non-/sense? Any idea how many decimals should be preserved for analysis? I might be hunting "fine digits" for nothing... In any case, conversions from DNs to Reflectance should be done in a 32-bit level [2] (that corresponds to: 1 / 2.328306e-10). The same is done with Landsat imagery though both L5 and L7 data are delivered as 8-bit [3]. Nikos --- [1] also mentioned in the document <http://www.digitalglobe.com/downloads/QuickBird_technote_raduse_v1.pdf>, page 7 [2] same document, page 8: "...conversion equations are to be performed on all pixels in a given band of a QuickBird image and should use 32-bit floating point calculations." [3] http://landsat.usgs.gov/how_is_radiance_calculated.php
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