i.fusion.hpf is an implementation of the High Pass Filter Additive
(HPFA) Fusion Technique. It combines 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 image to the statistical mean and standard
deviation of the original multi-spectral image.
- Computing ratio of low (Multi-Spectral) to high (Panchromatic)
- High Pass Filtering the Panchromatic Image
- Resampling MSX image to the higher resolution
- Adding weighted High-Pass-Filetred image to the upsampled MSX
- Optionally, matching histogram of Pansharpened image to the one of
the original MSX image
| Pan Img -> High Pass Filter -> HP Img |
| | |
| v |
| MSx Img -> Weighting Factors -> Weighted HP Img |
| | | |
| | v |
| +------------------------> Addition to MSx Img => Fused MSx Image |
Source: Gangkofner, 2008
- Grasping and testing the various parameters that define the High-Pass
filter's kernel size and center value is a matter of short time.
- Works with any number and type of raster imagery (8-bit, 16-bit)
- 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
option --a floating point "trimming factor" with which to multiply the
pixel size of the low resolution image-- and shrink the extent of the
The module is fairly easy to use. Arbitrary examples:
Pansharpening of one band:
i.fusion.hpf pan=Panchromatic msx=Red
Pansharpening of multiple bands:
i.fusion.hpf pan=Panchromatic msx=Red,Green,Blue,NIR
Various illustrated examples detailed in the document
i.fusion.hpf, implementation of the High Pass Filter Additive (HPFA) Image Fusion Technique (PDF)
- Go through Submitting Python
- Access input raster by row I/O
- Support for parallel processing
- Proper command history tracking.
- Add timestamps (r.timestamp, temporal framework)
- Deduplicate code where applicable
- Make verbose level messages shorter, yet more informative (ie report center cell)
- Test if it compiles in other systems
- Check options to integrate in i.pansharpen. Think of FFM methods vs. Others?
- Improve Documentation.lyx
- 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. ERDAS
- 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
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