221 
E r „(0,0) = 
/4 
(8) 
4. FUSION RESULT & ANALYSIS 
% n >,0)+ E^Oj)*' 
K E pemH (l,0) + E panH {l,l) j 
00 
01 
10 
11 
00 
01 
02 
03 
10 
11 
12 
13 
20 
21 
22 
23 
30 
31 
32 
33 
2-a Pan L 2-b Pan H 
Figure 2 Resampling Schematic Diagram 
The energy ratio images should be converted from low 
resolution to “high resolution” based on the principle that 1 
pixel of low resolution is divided into N pixels of high 
resolution, and every pixel value of “high resolution” equals to 
the pixel value of low resolution. 
3. SSVR FUSION ALGORITHM FLOW 
4.1 Study Area & Data 
The Study data covers Landsat7 ETM+ RS image obtained on 
July 1 st , 1999, and WRS id 123/032. Because scope of 
panchromatic wave band covers most part of visible light and 
part of near infrared and this band’s wavelength width is similar 
to that of band 2, 3, 4 of multispectral image, image covering 
these three bands is merged with panchromatic image and the 
spectra properties of fusion image are pretty similar to that of 
origin multispectral image. 
Table 1 Wavelength Region & Resolution of Selected RS 
Images 
RS Images 
Wavelength Region 
Resolution 
band2: 0.525-0.605 
ETM+ MS 
band3: 0.630-0.690 
30m 
band4: 0.775-0.90 
ETM+ Pan 
band8: 0.52—0.90 
15m 
Northern suburban areas of Beijing City are taken as study 
areas, and the square size is 30KM*30KM. Terrain and surface 
features in this area are buildings, roads, water and farm, etc. 
These objects are vary in shape and size, and RS images 
correspondingly vary in spectroscopy features, so fusion result 
can reflect merits and defects of algorithms. 
(1) Converting DN value to radiance. The conversion formula is 
4.2 Fusion Result 
DN K x (CT 
255 
According to the selected RS images, PCA, Multiplicative, 
(9) Brovey, ISVR and SSVR fusion are processed respectively and 
the result show below: 
where L k is radiance of band k, C k ,ax and C' k m are 
radiance correction coefficients, which can be acquired from 
head files, and DN k is digital number of band k. 
(2) Computing energy values of remote sensing images’ bands. 
The computing formula is 
E xs - L i x Wj (10) 
where £ is energy value of band i, f is radiance of band 
i, and jy. is width of band i .Take landsat7 ETM+ data as an 
example, the formula is £ = L s x (0.92 - 0.52 ) ° 
(3) According to formula (7), the pixel size of Pan images is 
resampled from 15m x 15m to 30m><30m. 
F 
(4) Computing x s Li . As the energy value results of MS 
E Pan L 
images band 2, 3, 4 and Pan Bands are computed previously, it 
is easy to get the value. 
(5) Computing XSP i according to formula (6). 
(6) Layerstack XSP t to make fusion effect evaluation. 
1 Display effect. 
Overall effect (Figure. 4), partial effect (Figure. 5). 
2^ Evaluation of Fusion Effect 
At all the research related to the multi-resource data fusion, 
there is no unique standard to evaluate the result of data fusion, 
because different data sources are introduced to the fusion. Up 
to now the main evaluation standard of the fusion is the 
combination of the quality analysis based on visual 
interpretation and the quantity analysis of statistics. 
From the view of image resolution, the resolution of all the 
image after fusion is enhanced, small avenue and the board of 
water are clearly than before. From the view of spectral 
information, the SSVR algorithms is the best, multiplicative 
algorithm is not as good as the former, and the brovey and PCA 
algorithms make large spectral distortion. The SSVR algorithm 
is picked for this research, since the research on land use and 
land cover, the spectral information is the most important factor. 
(1) Entropy: the number of entropy is directly response to 
the quantity of information. The high of the number of entropy, 
the more information the image after fusion carrying on. 
(2) Correlative Coefficient: the parameter stands for the 
relationship between the image before fusion and the image 
after fusion. From the parameter we can draw the conclusion 
that how many changes of spectral information could be found. 
In the algorithms used in the research, DN and Radiance are 
used in the image fusion (DN is used by PCA, Multiplicative 
and Brovey, radiance is used in SSVR). Entropy is used to