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       i.pansharpen    -   Image   fusion  algorithms  to  sharpen  multispectral  with  high-res
       panchromatic channels


       imagery, fusion, sharpen, Brovey, IHS, HIS, PCA


       i.pansharpen --help
       i.pansharpen [-sl] red=name green=name blue=name  pan=name  output=basename  method=string
       [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

           Serial processing rather than parallel processing

           Rebalance blue channel for LANDSAT

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           Force launching GUI dialog

       red=name [required]
           Name of raster map to be used for <red>

       green=name [required]
           Name of raster map to be used for <green>

       blue=name [required]
           Name of raster map to be used for <blue>

       pan=name [required]
           Name of raster map to be used for high resolution panchromatic channel

       output=basename [required]
           Name for output basename raster map(s)

       method=string [required]
           Method for pan sharpening
           Options: brovey, ihs, pca
           Default: ihs


       i.pansharpen  uses  a  high  resolution  panchromatic  band  from a multispectral image to
       sharpen 3 lower resolution bands. The 3 lower resolution bands can then be  combined  into
       an  RGB  color  image  at  a  higher (more detailed) resolution than is possible using the
       original 3 bands. For example, Landsat ETM has low resolution spectral bands 1  (blue),  2
       (green),  3  (red),  4 (near IR), 5 (mid-IR), and 7 (mid-IR) at 30m resolution, and a high
       resolution panchromatic band 8 at 15m resolution. Pan sharpening allows  bands  3-2-1  (or
       other  combinations of 30m resolution bands like 4-3-2 or 5-4-2) to be combined into a 15m
       resolution color image.
       i.pansharpen offers a choice of three different ’pan sharpening’ algorithms: IHS,  Brovey,
       and PCA.
       For  IHS pan sharpening, the original 3 lower resolution bands, selected as red, green and
       blue channels for creating an RGB composite image, are transformed  into  IHS  (intensity,
       hue,  and  saturation)  color  space.  The  panchromatic  band is then substituted for the
       intensity channel (I), combined with the original hue (H) and saturation (S) channels, and
       transformed back to RGB color space at the higher resolution of the panchromatic band. The
       algorithm for this can be represented as: RGB -> IHS -> [pan]HS -> RGB.
       With a Brovey pan sharpening, each of the 3 lower resolution bands and  panchromatic  band
       are  combined  using  the  following  algorithm  to  calculate  3  new bands at the higher
       resolution (example for band 1):
           new band1 = ----------------------- * panband
                        band1 + band2 + band3
       In PCA pan sharpening, a principal component analysis is performed on the original 3 lower
       resolution  bands  to  create  3  principal component images (PC1, PC2, and PC3) and their
       associated eigenvectors (EV), such that:
            band1  band2  band3
       PC1: EV1-1  EV1-2  EV1-3
       PC2: EV2-1  EV2-2  EV2-3
       PC3: EV3-1  EV3-2  EV3-3
       PC1 = EV1-1 * band1 + EV1-2 * band2 + EV1-3 * band3 - mean(bands 1,2,3)
       An inverse PCA is then performed, substituting the panchromatic band for PC1.  To do this,
       the  eigenvectors  matrix  is  inverted  (in  this  case  transposed),  the  PC images are
       multiplied by the eigenvectors with the panchromatic band substituted for PC1, and mean of
       each  band  is added to each transformed image band using the following algorithm (example
       for band 1):
       band1’ = pan * EV1-1 + PC2 * EV2-1 + PC3 * EV3-1 + mean(band1)
       The assignment of the channels depends on the satellite.  Examples  of  satellite  imagery
       with  high  resolution  panchromatic  bands,  and  lower resolution spectral bands include
       Landsat 7 ETM, QuickBird, and SPOT.


       The module currently only works for 8-bit images.
       The command temporarily changes the computational region to the  high  resolution  of  the
       panchromatic  band  during  sharpening  calculations,  then  restores  the previous region
       settings. The current region  coordinates  (and  null  values)  are  respected.  The  high
       resolution  panchromatic  image  is  histogram matched to the band it is replaces prior to
       substitution (i.e., the intensity channel for IHS sharpening, the low  res  band  selected
       for each color channel with Brovey sharpening, and the PC1 image for PCA sharpening).
       By  default,  the  command will attempt to employ parallel processing, using up to 3 cores
       simultaneously. The -s flag will disable parallel processing, but does  use  an  optimized
       r.mapcalc expression to reduce disk I/O.
       The  three pan-sharpened output channels may be combined with d.rgb or r.composite. Colors
       may be optionally optimized with i.colors.enhance.  While the resulting color  image  will
       be  at the higher resolution in all cases, the 3 pan sharpening algorithms differ in terms
       of spectral response.


   Pan sharpening comparison example
       Pan sharpening of a Landsat image from Boulder, Colorado, USA:
       # R, G, B composite at 30m
       g.region raster=p034r032_7dt20010924_z13_10 -p
       d.rgb b=p034r032_7dt20010924_z13_10 g=lp034r032_7dt20010924_z13_20
       # i.pansharpen with IHS algorithm
       i.pansharpen red=p034r032_7dt20010924_z13_30 green=p034r032_7dt20010924_z13_20
           blue=p034r032_7dt20010924_z13_10 pan=p034r032_7dp20010924_z13_80
           output=ihs321 method=ihs
       # ... likewise with method=brovey and method=pca
       # display at 15m
       g.region raster=ihs321_blue -p
       d.rgb b=ihs321_blue g=ihs321_green r=ihs321_red


       R, G, B composite of Landsat at 30m                          R, G, B composite of Brovey sharpened image at 15m

       R, G, B composite of IHS sharpened image at 15m              R, G, B composite of PCA sharpened image at 15m"

   Pan sharpening of LANDSAT ETM+ (Landsat 7)
       LANDSAT ETM+ (Landsat 7), North Carolina sample dataset:
       # original at 28m
       g.region raster=lsat7_2002_10 -p
       d.mon wx0
       d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30
       # i.pansharpen with IHS algorithm
       i.pansharpen red=lsat7_2002_30@PERMANENT \
         green=lsat7_2002_20 blue=lsat7_2002_10 \
         pan=lsat7_2002_80 method=ihs \
       # display at 14.25m
       g.region raster=lsat7_2002_ihs_red -p
       d.rgb r=lsat7_2002_ihs_red g=lsat7_2002_ihs_green b=lsat7_2002_ihs_blue
       # compare before/after (RGB support in "Advanced"):
       # optionally color balancing:
       i.colors.enhance r=lsat7_2002_ihs_red g=lsat7_2002_ihs_green b=lsat7_2002_ihs_blue


        i.his.rgb, i.rgb.his, i.pca, d.rgb, r.composite


           ·   Original Brovey formula reference unknown, probably...
               Roller, N.E.G. and Cox, S., (1980). Comparison of Landsat MSS and  merged  MSS/RBV
               data  for  analysis  of  natural  vegetation.   Proc.  of  the  14th International
               Symposium on Remote Sensing of Environment, San Jose, Costa Rica, 23-30 April, pp.

           ·   Amarsaikhan,   D.,   Douglas,   T.  (2004).  Data  fusion  and  multisource  image
               classification. International Journal of Remote Sensing, 25(17), 3529-3539.

           ·   Behnia, P. (2005). Comparison  between  four  methods  for  data  fusion  of  ETM+
               multispectral and pan images. Geo-spatial Information Science, 8(2), 98-103.

           ·   Du, Q., Younan, N. H., King, R., Shah, V. P. (2007). On the Performance Evaluation
               of Pan-Sharpening Techniques. Geoscience and Remote Sensing Letters,  IEEE,  4(4),

           ·   Karathanassi,  V.,  Kolokousis,  P.,  Ioannidou,  S. (2007). A comparison study on
               fusion methods  using  evaluation  indicators.  International  Journal  of  Remote
               Sensing, 28(10), 2309-2341.

           ·   Neteler,  M,  D.  Grasso,  I.  Michelazzi, L. Miori, S. Merler, and C.  Furlanello
               (2005). An integrated toolbox for image registration, fusion  and  classification.
               International Journal of Geoinformatics, 1(1):51-61 (PDF)

           ·   Pohl,  C, and J.L van Genderen (1998). Multisensor image fusion in remote sensing:
               concepts, methods and application. Int. J. of Rem. Sens., 19, 823-854.


       Michael Barton (Arizona State University, USA)
       with contributions from Markus Neteler (ITC-irst, Italy); Glynn  Clements;  Luca  Delucchi
       (Fondazione E. Mach, Italy); Markus Metz; and Hamish Bowman.

       Last changed: $Date: 2016-02-09 17:51:03 +0100 (Tue, 09 Feb 2016) $


       Available at: i.pansharpen source code (history)

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