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NAME

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

KEYWORDS

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

SYNOPSIS

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

   Flags:
       -s
           Serial processing rather than parallel processing

       -l
           Rebalance blue channel for LANDSAT

       -r
           Rescale  (stretch) the range of pixel values in each channel to the entire 0-255 8-bit
           range for processing (see notes)

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       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

       bitdepth=integer [required]
           Bit depth of image (must be in range of 2-30)
           Options: 2-32
           Default: 8

DESCRIPTION

       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):
                                band1
           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
       and
       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 * EV1-2 + PC3 * EV1-3 + 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.

NOTES

       The  module  works  for  2-bit  to  30-bit  images.  All  images are rescaled to 8-bit for
       processing. By default, the entire possible range for the selected bit depth  is  rescaled
       to  8-bit. For example, the range of 0-65535 for a 16-bit image is rescaled to 0-255). The
       ’r’ flag allows the range of pixel values actually present in an image rescaled to a  full
       8-bit  range.  For  example,  a  16 bit image might only have pixels that range from 70 to
       35000; this range of 70-35000 would be rescaled to 0-255.  This  can  give  better  visual
       distinction  to  features,  especially  when  the  range of actual values in an image only
       occupies a relatively limited portion of the possible range.
       i.pansharpen 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.

EXAMPLES

   Pan sharpening comparison example
       Pan sharpening of a Landsat image from Boulder, Colorado, USA (LANDSAT  ETM+  [Landsat  7]
       spectral bands 5,4,2, and pan band 8):
       # R, G, B composite at 30m
       g.region raster=p034r032_7dt20010924_z13_20 -p
       d.rgb b=p034r032_7dt20010924_z13_20 g=lp034r032_7dt20010924_z13_40
           r=p034r032_7dt20010924_z13_50
       # i.pansharpen with IHS algorithm
       i.pansharpen red=p034r032_7dt20010924_z13_50 green=p034r032_7dt20010924_z13_40
           blue=p034r032_7dt20010924_z13_20 pan=p034r032_7dp20010924_z13_80
           output=ihs321 method=ihs
       # ... likewise with method=brovey and method=pca
       # display at 15m
       g.region raster=ihs542_blue -p
       d.rgb b=ihs542_blue g=ihs542_green r=ihs542_red

       Results:

         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 \
         output=lsat7_2002_ihs
       # display at 14.25m
       g.region raster=lsat7_2002_ihs_red -p
       d.erase
       d.rgb r=lsat7_2002_ihs_red g=lsat7_2002_ihs_green b=lsat7_2002_ihs_blue
       # compare before/after (RGB support in "Advanced"):
       g.gui.mapswipe
       # optionally color balancing:
       i.colors.enhance r=lsat7_2002_ihs_red g=lsat7_2002_ihs_green b=lsat7_2002_ihs_blue

SEE ALSO

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

REFERENCES

           ·   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.
               1001-1007

           ·   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),
               518-522.

           ·   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.

AUTHORS

       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.

SOURCE CODE

       Available at: i.pansharpen source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual