Full text: Mapping without the sun

13 
npervious surface 
in environmental 
\ssociation 62(2): 
)02). "Impervious 
t literature and its 
mal of Planning 
>. "An Advanced 
ultitemporal SAR 
)SCIENCE AND 
t applications in 
nation and urban 
iE Trans. Geosci. 
Change Detection 
teristics of SAR 
Remote Sensing, 
rvious surface in 
I of Environment 
imperviousness." 
1. 
ure analysis for 
nagery." Remote 
>ach for mapping 
ise of Landsat 7 
Canadian Journal 
1 (2003). "Urban 
1 imperviousness 
Photogrammetric 
010. 
by Competitive 
; Research Grant 
il Science and 
>e University of 
to the Chinese 
iduate Research 
A NOVEL FUSION METHOD OF SAR 
AND OPTICAL IMAGES FOR URBAN OBJECT EXTRACTION* * 
Jia Yonghong a b,c , Rick S. Blum c ,Ma Yunxia 3 
3 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China - yhjia2000@sina.com, 
myx 162636@ 163 .com 
b State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China - 
yhjia2000@sina.com 
c Electrical and Computer Engineering Dept, Lehigh University, Bethlehem, PA USA-rblum@eecs.lehigh.edu 
Commission VII, WG VII/6 
KEY WORDS: Fusion, Image, method, SFIM, Texture, Information extraction 
ABSTRACT: 
A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR texture is 
extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high pass details extracted 
from the available Pan image by means of the a trous wavelet decomposition. Then, high pass details modulated with the texture is 
applied to obtain the fusion product by high pass filtering based on modulation (HPFM) fusion method. A set of image data 
including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the experiment. The results demonstrate accurate 
spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture 
information enhances the fusion product, and the proposed approach is effective for image interpret and classification. 
1. INTRODUCTION 
Image fusion is capable of integrating different imagery data 
creating more information than that from a single sensor, and it 
has received tremendous attention in the remote sensing 
literature. Many image fusion algorithms and software tools 
have been developed, such as the IHS (Intensity, Hue, 
Saturation), PCA (Principal Components Analysis), SVR 
(Synthetic Variable Ratio) and wavelet based fusion (Alparone 
et al, 2004). However, such available algorithms are not 
efficient for the fusion of SAR and optical images any more. In 
an urban area, many land cover types/surface materials are 
spectrally similar. This makes it extremely difficult to analyze 
an urban scene using a single sensor (Forster, 1985; Hepner and 
Houshmand, 1998). Some of these features can be discriminated 
in a radar image based on their dielectric properties and surface 
roughness. The objective of our study is to present a novel 
image fusion method of SAR, Panchromatic (PAN) and 
multispectral (MS) data for urban object extraction. SAR 
texture is extracted by ratioing the despeckled SAR image to its 
low pass approximation, and is used to modulate high pass 
details extracted from the available Pan image by means of the 
a trous wavelet decomposition. High pass details modulated 
with the SAR texture is applied with high pass filtering based 
on modulation (HPFM) to obtain the fusion product. The 
following is introduction of the proposed fusion method. 
2. METHODOLOGY 
2.1 A trous wavelet 
Wavelet transform produces the images in different resolution. 
Wavelet representation refers to both spatial and frequency 
space. It can show a good position of an image in spatial and 
frequency space(Ranchin and Wald , 2000) 
There are different approaches to do wavelet decomposition. 
One of them is Mallat algorithm which can use wavelet 
function such as Daubechies functions. Here we use the a trous 
algorithm, which uses dyadic wavelet to merge non-dyadic data 
in a simple and efficient procedure. In this algorithm for the 
discrete wavelet transform we must do the successive 
convolution with a filter. To convolve the image and the filter, 
we use convolution function directly. In each step we get a 
version of the image I u I 2 The wavelet coefficient is 
defined as the following 
wc L = I L . r I L L=\,2,...,n (1) 
If we decompose an image I into wavelet coefficients, then we 
can write 
n 
l='Z WC l +I r W 
L=\ 
in which I r is a residual image. In this approach all wavelet 
planes have the same number of pixels as the original image. 
The project supported by the State Surveying and Mapping Fund of China.yhjia2000@sina.com
	        
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