In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B
3.2.1 High Pass Filtering: For the spatial quality, we
compare the high frequency data from the panchromatic image
to the high frequency data from each band of the fused image
using a method proposed by Zhou in 2004. To extract the high
frequency data we apply the following convolution mask to the
images:
mask =
-1
-1
-1
-1 -1
8 -1
-1 -1
(4)
The correlation coefficients between the high-pass filtered
fusion results and the high-pass filtered panchromatic image is
used as an index of the spatial quality (Hong, 2007). The
principle is that the spatial information unique in panchromatic
image is mostly concentrated in the high frequency domain. The
higher correlation between the high frequency components of
fusion result and the high frequency component of
panchromatic image indicates that more spatial information
from panchromatic image has been injected into the fusion
result.
1
ag k =~
k (P-1XÔ-D
k = R,G,B
x=\
dF k (x,y) 2 BF k (x,y) j
dx dy
(5)
where F k (x,y) is the pixel value of the fused image at
position (x,y). The average gradient reflects the clarity of the
fused image. It can be used to measure the spatial resolution of
the fused image, i.e., a larger average gradient means a higher
spatial resolution (Li et al., 2005).
3.2.4 Entropy: Entropy as a measure to directly conclude
the performance of image fusion. The Entropy can show the
average information included in the image and reflect the detail
information of the fused image (Han et al.,2008). Commonly,
the greater the Entropy of the fused image is, the more abundant
information included in it, and the greater the quality of the
fusion is. According to the information theory of Shannon, The
Entropy of image is:
255
£=-^/’ 1 ° g2 /;
i=0
(6)
Where E is the Entropy of image, and P t is the probability of i
in the image.
4. EXPERIMENT DATA AND ANALYSIS OF FUSION
RESULTS
4.1 Experiment Data
Figure 2. Spatial quality assessment by high pass fdtering
3.2.2 Edge detection: In this method first detect the edges of
panchromatic and fused image by canny operator, the more
closely the edge data of the fused image matches the edge data
of the panchromatic, indicating better spatial quality.
Band 1
91.16%
Band 2
92.10%
Band 3
92.64 %
Mean
91.06%
Figure 3. Spatial quality assessment by edge detection
3.2.3 Average gradient: For the spatial quality, we use the
average gradient to evaluate the performance of the fused image
F. That is
The image fusion techniques applied on the IRS P5 and P6
satellite images. IRS-P6 multispectral image has three 5.8-m
resolution spectral bands (Green,Red,NIR) and resolution of
IRS-P5 panchromatic image is 2.5-m. The study area is chosen
to cover different terrain morphologies. Figure 4 shows an
example of the fused IRS-P6 MS and IRS-P5 pan images using
five fusion algorithms, such as Standard IHS, Modified IHS,
PCA, Brovey and wavelet algorithms.
4.2 Analysis of Fusion Results
Initial qualitative visual inspections reveal that all the fused
images have better qualifications than original non-fused
images. The sharpness of the fused images has been
significantly enhanced. The further quantitative evaluation can
be done with above criteria.
4.2.1 Spatial Quality Assessment: Figure 5 shows the
correlation coefficients between high pass filtered results and
high pass filtered panchromatic image, PC is the highest,
Standard IHS is the second and wavelet is the lowest. That
means the PC and Standard IHS fusion results are injected into
the most spatial information, while the wavelet fusion result is
injected into the least spatial information.
The average gradients of the images obtained by different
fusion algorithms are shown in figure 6. The ag of Standard IHS
is the highest in the five algorithms, and ag of PC and Modified
IHS is the further maximum, therefore, the Standard IHS-fused
image has absorbed the high spatial frequency information most
and thus shows sharper than the others.
