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In the mid-1980s, image fusion received significant attention from researchers in remote sensing and image processing,
as SPOT 1 (launched in 1986) provided high-resolution (10m) Pan images and low-resolution (20m) MS images. Since
that time, much research has been done to develop effective image fusion techniques. Image fusion is a technique used to
integrate the geometric detail of a high-resolution panchromatic (Pan) image and the color information of a lowresolution
multispectral (MS) image to produce a high-resolution MS image.
Many methods such as Principal Component Analysis (PCA), Multiplicative Transform, Brovey Transform, and IHS
Transform have been developed in the last few years producing good quality fused images. These images are usually
characterized by high information content, but with significantly altered spectral information content. There are also
some limitations in these fusion techniques. The most significant problem is color distortion. A major reason for the
significant color distortion in fusion provoked by many fusion techniques is the wavelength extension of some satellite
panchromatic images. Unlike the panchromatic image of the SPOT and IRS sensors, the wavelength range of the new
satellites is extended from the visible into the near infrared. This difference significantly changes the gray values of the
new panchromatic images. Therefore, traditional image fusion techniques - useful for fusing SPOT Pan with other MS
images - cannot achieve quality fusion results for the new satellite images.
More recently new techniques have been proposed such as the Wavelet Transform, the Pansharp Transform and the
Modified IHS Transform. Those techniques seem to reduce the color distortion problem and to keep the statistical
parameters invariable.
Ideally, the methods used to fuse image data sets should preserve the spectral characteristics of the original multispectral
input image. While many technologies exist and emphasize the preservation of spectral characteristics, they do not take
into account the resolution ratio of the input images. Usually the spatial resolution of the panchromatic image is two
(Landsat 7, Spot 1-4) or four times (Ikonos, Quickbird) better than the size of the multispectral images. This paper is an
attempt to fuse high-resolution panchromatic and low-resolution multispectral bands of the EO-1 ALI sensor. ALI
collects nine multispectral bands with 30m resolution and a panchromatic band with 3 times better resolution (10m). ALI
has a panchromatic band narrower than the respective band of Landsat7. It has also two narrower bands in the spectral
range of Landsat7 band 4. It has also an extra narrower band near the spectral range of Landsat7 band 1.
In this study we compare the efficiency of seven fusion techniques and more especially the efficiency of Gram Schmidt,
Modified IHS, PCA, Pansharp, Wavelet and LMM (Local Mean Matching) LMVM (Local Mean and Variance
Matching) fusion techniques for the fusion of ALI data. Two ALI images collected over the same area have been used.
In order to quantitatively measure the quality of the fused images we have made the following controls: Firstly, we have
examined the optical qualitative result. Then, we examined the correlation between the original multispectral and the
fused images and all the statistical parameters of the histograms of the various frequency bands.
All the fusion techniques improve the resolution and the optical result. In contrary to the fusion of other data (ETM,
Spot5, Ikonos and Quickbird) all the algorithms provoke small changes to the statistical parameters.
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