Image fusion and
pan-sharpening software FREELY available:
For medium-resolution imagery:
Transformer for Landsat-8 imagery
* For high-resolution imagery: GUI-based HighView since 10/2011
* For global satellite imagery
Land Surface 2000 Version 3
As an example of image
fusion, pan-sharpening describes a process of
transforming a set of coarse (low) spatial resolution
multispectral (colour) images to fine (high) spatial
resolution colour images, by fusing a co-georegistered fine
spatial resolution panchromatic (black/white) image.
Typically, three low-resolution visible bands - blue,
green and red - are used as main inputs in the process
to produce a high-resolution natural (true) colour
image. Figure 1 illustrates this: the middle image is
a natural colour image with a spatial resolution of
2.4 m (resampled 400%), and the left image a
panchromatic image with a spatial resolution of 0.6 m;
by combining these inputs, a high-resolution colour
image is produced. In the fused output, spectral
signatures of the input colour image and spatial
features of the input pan image, as the best
attributes of both inputs, are (almost) retained. The
spectrally and spatially enhanced image is often
visually appealing, starting to compete with
high-resolution aerial photographs.
Image fusion is a concept of combining multiple images into composite products, through which more information than that of individual input images can be revealed.
Figure 1: Image pan-sharpening with QuickBird images. The left and middle input images are obtained from DigitalGlobe, and the right-hand image is the fused result from HighView. All images are subject to the same histogram stretch.
|Figure 2: Image pan-sharpening with IKONOS images. The left and middle input images are obtained from Space Imaging, and the right-hand image is the fused result from HighView.|
The past few decades have seen quite a few image fusion and pan-sharpening methods in the public domain, including those based on multi-resolution wavelet transforms, PCA (Principal Component Analysis) transforms, and IHS (Intensity-Hue-Saturation) transforms. These methods, however, largely disregard important spectral characteristics of specific satellite sensors, therefore no consistent, colour-preserving results can be achieved. In other words, a simple data-driven approach without referring to spectral evidence can hardly produce satisfactory outcomes. This becomes more apparent when the recent generation of high- and medium-resolution satellite images is available, where there are marked spectral disparities between colour bands and the panchromatic band.
For more information
on spectral features and curves of recent satellite
sensors, please refer to websites of major image
www.digitalglobe.com (QuickBird, WorldView-2)
www.geoeye.com (IKONOS, GeoEye-1)
physical evidence of spectral characteristics and
applies optimisation methods to perform ideal image
pan-sharpening. While the fused imagery at a finer
spatial resolution still retains the mean of the
coarse-resolution input, its standard deviation
should naturally become larger due to
increased details and heterogeneity of image
features at finer resolutions (Figure 3). These
important characteristics set HighView apart
from other pan-sharpening methods on the market that
incorrectly claim the preservation of standard
deviation of the fused imagery at a finer
Figure 3: Statistical changes from
coarser-resolution input to fused,
finer-resolution imagery in HighView.
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All Pléiades, WorldView-1, QuickBird, GeoEye-2, IKONOS, SPOT-5 and Landsat images used at GeoSage website are demonstration, sample, or free images from Astrium, DigitalGlobe, Space Imaging, SPOT Image, NASA, USGS, GeoGratis and GLCF. The following banner picture is created by http://wordle.net/