The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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Fig.7 Fusion Image By PCA 
Quantitative Analysis 
Entropy analysis is chosen for quantitative analysis in this 
study. In terns of Shannon principle, the entropy can be 
calculated by equation(2) 
H = -^ j p i x\og 2 p i ( 2 ) 
The entropies of original data and the fusion images are 
calculated and listed in the following table. 
image 
entropy 
image 
entropy 
Fig.l 
R 
7.5164 
Fig.6 
R 
6.5786 
G 
7.5169 
G 
6.3843 
B 
7.4070 
B 
7.0040 
Fig.3 
5.6076 
Fig.7 
R 
7.7382 
Fig.4 
4.6589 
G 
7.8019 
Fig.5 
R 
6.1682 
B 
7.6527 
G 
5.9279 
B 
6.3698 
Table.2 Entropy Contrast Table 
In Table.2, the entropy values of Fig.l, Fig.2, Fig.3 shows the 
information contained in the corresponding images or band 
before fusion. Fig.5, Fig.6, Fig.7 represent the fusion images 
by IHS transform and PCA. The entropies are listed in Table.2. 
It is quite obvious that the entropy values of Fig.5 and Fig.6 is 
lower than Fig.l, but higher than Fig.3 and Fig.4. It means the 
spectral information in Fig.5 and Fig.6 is lost during the fusion 
process from Fig.l. Of course, after fusion, the information 
content have been increased from Fig.3 and Fig.4. 
The entropy value of Fig.7 is higher than that of Fig.l, Fig.2 
and Fig.3. It means that information in Fig.7 is more abundant 
than this images. Also, the entropy values are higher than that 
of Fig.5 and Fig.6. It indicates that the fusion result by PCA is 
better than result by IHS transform. 
3.2 Vector Fusion between LIDAR points and images 
Fusion discussed in section 3.1 indicates that fusion between 
raster points and Ortho-photomap. The information in these 
two data sources are complemented by each other. It is also 
clear that some of the spectral information is lost during the 
fusion procedure. Since the data source are all in raster format, 
this technology is also called raster fusion. 
Here we use vector Fusion to denote the fusion process 
between LIDAR points and images. When conducting the 
fusion, the LIDAR points are in vector format. This technology 
is used for appending the spectral information to every points 
according to the location relationship. It also means that not 
only coordinate data but also spectral and optical information 
are included in results. 
Since the coordinate relationship between points and images, 
the spatial analysis tech can be used for vector fusion. The 
most useful method for fusion is overlay analysis. For each 
LIDAR point, the corresponding pixel which lies in the same 
coordinate with the point is selected firstly. Then the spectral 
data, usually in R, G and B, are acquired and attached in the 
point. So, the data components of fused vector points are here 
listed: 
Coordinate _XCoordinate _Y,Coordinate _Z,Intensity 
The forth experiment is executed for Ortho-photomap and 
vector image of points cloud. Here, the I values of Ortho 
photomap are replaced by the grey value of interpolation 
results. Fig.5 shows the result of the first experiment. 
Fig. 8 Fusion Image by Overlay Analysis Method 
4. CONCLUSION 
Two kinds of method is executed for fusion between airborne 
laser scanning points and Ortho-photomap, raster fusion and 
vector fusion. In the part of raster fusion, the IHS transform 
and PCA algorithm are used for the integration of points data 
which have been interpolated into raster and raster image data. 
While in the part of vector fusion, the overlay analysis 
technology, which is a part of spatial analysis, is used to 
complete the integration of points and image. In terms of 
fusion images, both of the methods is helpful. But from 
quantitative analysis, the result from IHS transform is not as