Full text: Mapping without the sun

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
	        
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