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Title
Mapping without the sun
Author
Zhang, Jixian

information than (e) and (f).
pp.1245-1257
215
Then we evaluate the performance of the fusion method using
some image quality indexes. The indexes we selected are
average value, standard difference, entropy, average grads and
correlated coefficient. Average value can show the distribution
of the image grayscale in the roughness. Standard difference
and entropy can measure the information abundance in the
image. Average grads shows exiguous contrast, varied texture
characteristic and definition of the image. Correlated
coefficient is calculated between fused image and SPOT5
multi-spectral image, which shows how much spectral
information have been conserved. The statistics is shown in tab
1.
Average values in table 1 show that the mean gray level of
fused image is very close to the SPOT5 image. Standard
difference, entropy and average grads in table 1 show that the
information insufficiency of SAR image has decreased the
information insufficiency of fused images, but the fused images
with DT-CWT have conserved more information than DWT at
corresponding level. Correlated coefficients in table 1 show
that spectral characteristics of the fused images with DT-CWT
are closer to the multi-spectral SPOT5 image than DWT at
corresponding level.
In a word, not only at information enhancement but also at
spectral information conservation, fusion based on DT-CWT is
superior to DWT.
5. CONCLUSIONS
In this paper, the dual-tree complex wavelet transform has been
used to fuse SAR and optical images. According to the
characteristic of SAR image, it is first denoised by Gamma
MAP and Lee-Sigma filter. And then the low and high
frequency parts of decomposed SAR and optical images have
been fused by maximum gray value rule and maximum energy
value rule respectively. Finally images fused by DT-CWT and
DWT at different level have been compared in experiments.
Observation results and statistics of quality indexes have shown
that the fusion algorithm based on DT-CWT was better than
DWT.
REFERENCES
A. Jalobeanu , L. Blanc-Féraud , J. Zerubia, 2000, Satellite
image deconvolution using complex wavelet packets, Thème 3-
Interaction homme-machine,images, données, connaissances
Projet Ariana Rapport de recherche, pp.3955-4071
Baraldi A. , F. Parmiggiani, 1995, A refined gamma map sar
speckle filter with improved geometrical adaptivity, IEEE
Transactions on Geoscience and Remote Sensing, 33(5),
Javier Portilla , Eero P. Simoncelli, 1999, Texture modeling
and synthesis using joint statistics of complex wavelet coeffici
ents, Proceedings of the IEEE Workshop on Statistical and
Computational Theories of Vision, Fort Collins, CO,
http://www.cis.ohio-state.edu/_szhu/SCTV99.html
Jiang Han-ping, Wang Jian-min, 2000, Application of
complex-valued wavelet transform to image matching, Journal
of Jiang-xi Institute of Education (Natural Science), 21(6),
pp.29-31
Julian Magarey, Nick Kingsbury, 1998, Motion estimation
using a complex valued wavelet transform, IEEE Trans, on
Signal Processing, special issue on wavelets and filter banks,
46(4), pp. 1069-1084
Lee S. J., 1980, Digital image enhancement and noise filtering
by use of local statistics, IEEE Transactions on Pattern
Analysis and Machine Intelligence, PAM 1-2 (2)
Lopes A., E. Nezry, R. Touzi, H. Laur, 1993, Structure
detection and statistical adaptive speckle filtering in sar images,
Int. J. Remote Sensing, 14(9), pp.1735-1758
N. Kingsbury, 1998a, The dual-tree complex wavelet transform:
A new technique for shift-invariance and directional filters, in
DSP Workshop
Nick Kingsbury, 1998b, The dual tree complex wavelet
transform: A new efficient tool for image restoration and
enhancement, Proc. European Signal Processing Conference,
EUSIPCO 98, Rhodes, pp.319-322
Peter de Rivaz and Nick Kingsbury, 2001, Bayesian image dec
onvolution and denoising using complex wavelets, Proc. IEEE
Conf. on Image Processing, Greece, Oct 8-10, paper 2639.
Serkan Hatipoglu, Sanjit K. Mitra, and Nick Kingsbury, 1999,
Texture classification using dual-tree complex wavelet
transform, IEE. Image Processing and its Applications,
Conference Publication No.465
S G Mallat, 1989, A theory for multiresolution signal
decomposition: The wavelet representation, IEEE Trans. PA MI,
11(7), pp.674-693
Xiao Guochao, Zhu Caiying, 2001, Radar photogrammetry,
Earthquake press, Beijing, pp.35
Images
Average Value
Standard
Difference
Entropy
Average Grads
Correlated
Coefficients
SAR
53.340197
26.879212
5.037007
8.630476

SPOT5 multi-spectral
94.890384
27.411184
11.468343
12.181283

Fused by 1-level DWT
97.061614
29.517138
11.524848
11.650854
0.836114
Fused by 2-level DWT
96.100397
27.015098
11.397434
9.548232
0.843378
Fused by 3-level DWT
95.309379
25.254701
11.284076
8.770403
0.833622
Fused by 1-level DT-CWT
97.127747
29.676878
11.560681
12.729831
0.873973
Fused by 2-level DT-CWT
96.097643
27.800316
11.476087
11.813529
0.914931
Fused by 3-level DT-CWT
95.367769
26.061456
11.375349
11.354203
0.908619
Table 1. Statistics of quality indexes of the images in figure 4 and 5