[GRASS-dev] [GRASS GIS] #2048: i.pansharpen limited to 8-bit imagery

GRASS GIS trac at osgeo.org
Wed Jul 31 04:49:41 PDT 2013


#2048: i.pansharpen limited to 8-bit imagery
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 Reporter:  nikosa                                                     |       Owner:  grass-dev@…              
     Type:  defect                                                     |      Status:  new                      
 Priority:  normal                                                     |   Milestone:  7.0.0                    
Component:  Imagery                                                    |     Version:  svn-trunk                
 Keywords:  i.pansharpen, sharpening, fusion, brovey, ihs, pca, 8-bit  |    Platform:  Unspecified              
      Cpu:  All                                                        |  
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Comment(by mlennert):

 Replying to [comment:9 cmbarton]:
 > For pan sharpening, the reason for histogram matching is that the higher
 resolution pan image needs to replace one of the lower resolution images
 as part of the sharpening algorithm. So the pan band needs to be histogram
 matched to the one it is replacing. This is why I don't think that
 matching it to an average of all bands is a good idea. I could be
 mistaken, but it seems incorrect conceptually.

 I agree with you that for i.pansharpen that's the way to go. What I said
 is that I think i.histo.match should ideally allow the use to chose
 between the different techniques.


 >
 > The routine I used is the standard one discussed for this. There may
 well be a more accurate one and a better way to deal with floating point
 and large integer data. Using numpy arrays makes this one quite fast. It
 might benefit from parallelization in some way. I parallelized other parts
 of the code but not this one. So, yes, if there is a student out there
 with not enough to do...   ;-)

 In terms of optimisation I was mostly thinking about the double loop
 through arrays[target] within the 'for i in arrays[original]' loop. I
 think that there are more efficient search algorithms, but yours
 definitely works.

 Moritz

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Ticket URL: <https://trac.osgeo.org/grass/ticket/2048#comment:10>
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