3, 2012
produced
Saturation
in 3 steps:
npled and
and is then
: The HIS
tral image
'omponent
a. The PC
which is
1e spectral
erformed;
0 the high
ir or cubic
time and
o wavelet
of signal
e signal at
tages over
situation
spikes so
scales or
is defined
by scaled,
t is shown
(1)
and high
also be
'ed in this
rently, it
nethod for
se images
the image
cs of each
different
focus on
nonitoring
yavelets is
es images
efficients
for fusion
nilies are
Symlets,
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elet based
dbl) was
based on
> wavelet
in a data
sult so we
the high
at yellow
so fusion
could lead us to better extraction of moving object for helping
to have automatic monitoring system.
Figure2. The First image acquired in T1
F ure3. The Second image acquired in T2
Figure4. The fused image based on wavelet theory
5. CONCLUSION
The main objective of this study was to overcome the present
problems of automatic monitoring with multi time’s image
fusion. Wavelet theory was used in this study as a good method
for fusion at the pixel level. It decomposes an image in to low
frequency band and high frequency band in different levels, so
we could integrate multi images for moving object tracking
what’s more during our process we tried to select the best form
of wavelet and decomposition level for getting to the best
quality for fusion of images.
The result of this analysis will help us to implement automatic
system that could monitor moving object without human
intervention.
6. REFERENCES
1. Hall,D.,2001. Handbook of multisensor data fusion, CRC
Press.
2. Nahin, Pokoski,1980. Multi sensor Data Fusion.
3. Kale,V.,2010. Performance Evaluation of Various Wavelets
for Image Compression of Natural and Artificial Images.
4.Wassai,A.,2011, Arithmetic and Frequency Filtering Methods
of Pixel-Based Image Fusion Techniques India.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
