i.fusion.hpf is a GRASS-GIS module to combine high-resolution panchromatic data with lower resolution multispectral data, resulting in an output with both excellent detail and a realistic representation of original multispectral scene colors.

The process involves a convolution using a High Pass Filter (HPF) on the high resolution data, then combining this with the lower resolution multispectral data.

Optionally, a linear histogram matching technique is performed in a way that matches the resulting Pan-Sharpened imaged to them statistical mean and standard deviation of the original multi-spectral image.

Source: Gangkofner, 2008

1. Computing ratio of low (Multi-Spectral) to high (Panchromatic) resolutions

2. High Pass Filtering the Panchromatic Image

3. Resampling MSX image to the higher resolution

4. Adding weighted High-Pass-Filetred image to the upsampled MSX image

5. Optionally, matching histogram of Pansharpened image to the one of the original MSX image

Step 1: HP Filtering of the High-resolution Image to Extract the Structural Detail

Step 2: Adding the HP Filtered Image to Each Band of the Multispectral Image Using a Standard Deviation-based Injection Model

Step 3: Linear Histogram Match to Adapt SD and Mean of the Merged Image Bands to Those of the Original MS Image Bands

Figure 1:

 ____________________________________________________________________________
+                                                                            +
| Pan Img ->  High Pass Filter  ->  HP Img                                   |
|                                      |                                     |
|                                      v                                     |
| MSx Img ->  Weighting Factors ->  Weighted HP Img                          |
|       |                              |                                     |
|       |                              v                                     |
|       +------------------------>  Addition to MSx Img  =>  Fused MSx Image |
+____________________________________________________________________________+

see GRASS Addons SVN repository, README file, Installation - Code Compilation

Making the script i.fusion.hpf available from within any GRASS-GIS ver. 7.x session, may be done via the following steps:

1. launch a GRASS-GIS’ ver. 7.x session

2. navigate into the script’s source directory

3. execute make MODULE_TOPDIR=$GISBASE

After installation, from within a GRASS-GIS session, see help details via i.fusion.hpf --help

• easy to use, i.e.:

• for one band i.fusion.hpf pan=Panchromatic msx=${Band}
• for multiple bands i.fusion.hpf pan=Panchromatic msx=Red,Green,Blue,NIR

• easy to test various parameters that define the High-Pass filter’s kernel size and center value

• should work with any kind of imagery (think of bitness)

• the "black border" effect, possibly caused due to a non-perfect match of the high vs. the low resolution of the input images, can be trimmed out by using the trim option --a floating point "trimming factor" with which to multiply the pixel size of the low resolution image-- and shrink the extent of the output image

• First commit on Sat Oct 25 12:26:54 2014 +0300

• Working state reached on Tue Nov 4 09:28:25 2014 +0200

• Go through http://trac.osgeo.org/grass/wiki/Submitting/Python

• Access input raster by row I/O ?

• Proper command history tracking. Not all "r" modules do it... ?

• Add timestamps (r.timestamp)

• Deduplicate code where applicable

• Make the -v messages shorter, yet more informative (ie report center cell)

• Test. Will it compile in other systems?

• Checking options to integrate in i.pansharpen. Think of FFM methods vs. Others?

• Who else to thank? Transfer from archive/

• Improve Documentation.lyx

• To Ask!

• Gangkofner, U. G., Pradhan, P. S., and Holcomb, D. W. (2008). Optimizing the high-pass filter addition technique for image fusion. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 74(9):1107–1118.

• “ERDAS IMAGINE.” Accessed March 19, 2015. http://doc.hexagongeospatial.com/ERDAS%20IMAGINE/ERDAS_IMAGINE_Help/#ii_hpfmerge_mergedialog.htm.

• Aniruddha Ghosh & P.K. Joshi (2013) Assessment of pan-sharpened very high-resolution WorldView-2 images, International Journal of Remote Sensing, 34:23, 8336-8359

• Nikos Ves
• Ranjith, https://class.coursera.org/interactivepython-005/forum/profile?user_id=9361576
• Anonymous on coursera's discussion forums
• Pietro Zambelli
• StackExchange contributors
  • http://stackoverflow.com/a/1140966/1172302
  • http://stackoverflow.com/a/275025/1172302
• Yann Chemin
• Aniruddha Ghosh