Spatial Resolution= 2.44m Spatial Resolution=0.6m
INPUT IMAGES- Quickbird
1. MULTIPLICATIVE ALGO:
The multiplication model combines two data sets by multiplying each pixel in
each band of the MS data by the corresponding pixel of the pan data
(pohl.C,1997). To compensate for the increased brightness values (BV), the
square root of the mixed data set is taken.
METHODS OF PIXEL LEVEL FUSION
• Simple & Straight Forward.
• Alters the spectral information of the
Since the original Brovey Transform can only allow three bands to be fused, the
transform has to be modified.
• The Modified Brovey algorithm is a ratio method where the data values of each
band of the MS data set are divided by the sum of the MS data set and then
multiplied by the Pan data set.
• Increases the contrast in the low and high
ends of an image histogram.
• Three bands at a time should be merged
from multispectral scene.
• It should not be used if preserving the
original scene radiometry is important.
Subtractive Resolution Merge uses a subtractive algorithm to pan sharpen
multi-spectral (MS) images.
Specifically, it was designed for Quickbird, Ikonos and Formosat images
that have simultaneous acquisition of the pan and MS, with all 4 MS bands
present, and a ratio between the MS and pan image pixels sizes of
approximately 4:1. Other sensors that have similar capabilities should also
work well with this algorithm.
• Produces highly preserved spatial and spectral
• Limited to dual sensor platforms with specific
band ratios between the high-resolution
panchromatic image and the low-resolution
multispectral image .
4. WAVELET METHOD:
The wavelet transform decomposes the signal based on elementary
functions: the wavelets.
By using this, a digital image is decomposed into a set of multi resolution
images with wavelet coefficients. For each level, the coefficients contain
spatial differences between two successive resolution levels.
• Minimizing colour distortion.
• Poor directional selectivity for diagonal
features, because the wavelet features are
separable and real.
COLOR RELATED TECHNIQUES:
1. IHS METHOD:
The IHS transform separates spatial (intensity) and spectral (hue and
saturation) information from a standard RGB image. The intensity
refers to the total brightness of the image, hue to the dominant or
average wavelength of the light contributing to the colour and
saturation to the purity of colour.
• preserve more spatial feature and more required
functional information with no color distortion.
• only three bands are involved.
PC transform is a statistical technique that transforms a multivariate dataset of
correlated variables into a dataset of uncorrelated linear combinations of the
For images, it creates an uncorrelated feature space that can be used for further
analysis instead of the original multispectral feature space. The PC is applied to
the multispectral bands. The panchromatic image is histogram matched to the
It then replaces the selected component and an inverse PC transform takes the
fused dataset back into the original multi spectral feature space (Chavez et al.
• No. Of bands is not restricted.
• Sensitive to the area to be sharpen and
produces fusion result that may vary
depending on the selected image subset.
2. GRAM SCHMIDT:
The GS process transforms a set of vectors into a new set of orthogonal
and linear independent vectors.
By averaging the multispectral bands, the GS fusion simulates a low-
resolution panchromatic band.
As the next step, a GS transform is performed for the simulated
panchromatic band and the multispectral bands with the simulated
panchromatic band applied as the first band.
Then the high spatial resolution panchromatic band replaces the first GS
Finally, an inverse GS transform is applied to create the pan-sharpened
multispectral bands (Laben et al. 2000).
3. HPF METHOD:
High-pass filter fusion method is a method that make the high frequency
components of high-resolution panchromatic image superimposed on low-
resolution multispectral image, to obtain the enhanced spatial resolution
• preserves a high percentage of the spectral
characteristics, since the spatial information is
associated with the high-frequency information
of the MS, which is from the PAN, and the
spectral information is associated with the low-
frequency information of the MS, which is
from the PAN.
FEATURE LEVEL TECHNIQUES
It is based on an IHS transform coupled with a Fourier domain filtering.
The principal idea behind a spectral characteristics preserving image
fusion is that the high-resolution image has to sharpen the multispectral
image without adding new grey level information to its spectral
An ideal fusion algorithm would enhance high-frequency changes such
as edges and grey level discontinuities in an image without altering the
multispectral components in homogeneous regions.
To facilitate these demands, two prerequisites have to be addressed.
First, colour and spatial information have to be separated.
Second, the spatial information content has to be manipulated in a way
that allows an adaptive enhancement of the images. This is achieved by
a combination of colour and Fourier transforms.
Quality assessment is application dependant so that different applications
may require different aspects of image quality.
Bias of mean
QUALITATIVE TEST QUANTITATIVE- STATISTICAL TEST
VISUAL INTERPRETATION SPECTRAL EVALUATION SPATIAL EVALUATION
Root Mean Square
QUALITATIVE(OR SBJECTIVE) TEST
Qualitative methods involve visual comparison between a
reference image and the fused image
According to prior assessment criteria or individual experiences, personal
judgment or even grades can be given to the quality of an image.
The interpreter analyses the tone, contrast, saturation, sharpness, and
texture of the fused images.
Easier to interpret.
They are subjective and depend heavily on the experience of the
Cannot be represented by mathematical models, and their technique is
QUANTATIVE(OR OBJECTIVE) TEST
Measures spectral and spatial similarity between reference and fused images.
A) Spectral evaluation:
These methods should be objective, reproducible, and of quantitative
PARAMETERS FOR SPECTRAL EVALUATION :
1. Root mean square error (RMSE) : Proposed by Wald (2002). It is
computed by the difference of the standard deviation and the mean of
the fused and the original image. The best possible value is 0.
2. Correlation coefficient (CC): Measures the correlation b/w original
multispectral bands and the equivalent fused bands. It is the most
frequently used method to evaluate the spectral value preservation.
The values range from -1 to +1. The best correspondence between fused
and original image data shows the highest correlation value and should
be close to 1.
3. Bias of Mean: BM is the difference between the means of the original
MS image and of the fused image (stanislas de bethune,1998).
The value is given relative to the mean value of the original image. The
ideal value is zero.
B) Spatial evaluation:
1. Entropy: Entropy is defined as amount of information contained in an
image. Shannon was the first person to introduce entropy to quantify the
If entropy of fused image is higher than parent image then it indicates
that the fused image contains more information.
2. High-pass correlation (HCC) coefficient: A HP filter is first applied to
the panchromatic image and to each band of the fused image. Then the
correlation coefficients between the HP filtered bands and the HP filtered
panchromatic image are calculated.
3. Edge detection (ED) : An edge detector is applied to the panchromatic
image and each band of the fused multispectral image. The detected
edges are then compared to the panchromatic image edges for each
ED correspondence is measured in per cent; 100% means that all the
edges in the panchromatic image are detected in the fused image.
Standard Deviation: SD is an important index to weight the information
of image, it reflects the deviation degree of values relative to the mean of
the image. The greater SD represents greater amount of variation.
1. Intelligent robots
2. Medical image
4. Military and law enforcement
Increases the capability for enhancing features.
LU Classification of MS image LU Classification of fused image
ACCURACY FOR BUILD UP AREAS:
Increases classification accuracy.
Change detection is the process of identifying differences in the state
of an object or phenomenon by observing it at different times .Change
detection is an important process in monitoring and managing natural
resources and urban development.
Manfred Ehlers , Sascha Klonus , Pär Johan Åstrand & Pablo Rosso (2010)
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fusion of high spatial and spectral resolution data based on oversampled
multiresolution analysis. IEEE Trans. Geosci. Remote Sensing 40(10), 2300–
Ehlers, M., Klonus, S., Åstrand, P.J., 2008. Quality assessment for multi-
sensor multi-date image fusion. The International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol.
XXXVII. Part B4.
Dr. Nikolaos Mitianoudis, “Image fusion: theory and application,”