The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
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3. SEGMENTATION EXPERIMENT 
3.1 Experiment data 
To evaluate the performance of the proposed segmentation 
approach, a multi-spectral QuickBird imagery at 2.44-m 
resolution and a panchromatic QuickBird imagery at 0.61-m 
resolution, which were acquired in May 2005 in HeFei city of 
China were used. The area is about 1023 X 822 pixels and 
represents a complex urban environment. The selected part of 
the city is characterized by classes of road, highway, grass, and 
building. Initially, the QuickBrid imagery were geometrically 
corrected to the universal transverse Mercator (UTM) 
projection, and re-sampled to 0.61-m to match the image pixel 
size, and then fused by the Smoothing Filter-based Intensity 
Modulation(SFIM) method using the CASM Imagelnfo® 
remote sensing imagery processing software (CASM Imageinfo, 
2007) developed by Chinese Academy of Surveying and 
Mapping. The SFIM is a superior fusion technique for 
improving spatial detail of multispectral images with their 
spectral properties reliably preserved (Liu, 2000), and fusion 
strategy is helpful for improving classification accuracy (Xu, 
2004; Cao,2006). Figure 3(a) shows the panchromatic 
QuickBird image, and figure 3(b) shows the multi-spectral 
QuickBird composite image of band 4(infra-red), band 2(green), 
and band l(blue). Figure 3(c) shows the fused image 
compositing from the same bands as figure 3(b). 
3.2 Segmentation Experiment 
Initially, we segmented the fusion image by the improved SRM 
segmentation algorithm to get the initial segmentation result on 
the basis of the SRM software package. The key issue is trying 
to adjust parameter until getting better initial segmentation 
result, then these segmented imagery was vectorized in 
coverage format using ERDAS imagine 8.7® (ERDAS imagine, 
2003). Figure 3(d) shows the vector image overlaid on the 
initial segmentation result. 
Then, the objects from the initial segmentation were merged by 
the MHR method, meanwhile the objects information such as 
mean, boundary length, deviation is recomputed for latter 
merging operation. Figure 3(e) and figure 3(f) show the course 
scale imageries when the scale threshold is 40 and 60 
respectively. Table 1 shows the detail parameters. 
Segmentation 
level 
Shape 
Scale Color J 
W i m,oth ^CCMP! numbers 
Level 1 
Level 2 
Level 3 
8 0 0 0 702 
40 0.6 0.3 0.7 374 
60 0.5 0.2 0.8 185 
Table 1. The parameters of the new method 
In order to evaluate the performance of the proposed method, 
we also segmented the fused QuickBird imagery using FNEA 
that the eCognition soft adopted. The multi-scale segmentation 
results are shown in figure 3(g), figure 3(h), figure 3(i), and 
detail parameters are shown in table 2. Moreover, we carried on 
multi-scale SRM segmentation, and the results are shown in 
figure 3(j), figure 3 (k), figure 3 (1), and detail parameters are 
shown in table 3. 
Segmentation 
level 
Shape 
Seal. Colo, — 0bje , C ' 
W >m>o,h w ccmp> numbers 
Level 1 
Level 2 
Level 3 
100 0.6 0.3 0.7 528 
150 0.6 0.3 0.7 270 
180 0.5 0.3 0.7 186 
Table 2. The parameters of eCognition 
Segmentation 
level 
Scale 0bi "* 
numbers 
Level 1 
Level 2 
Level 3 
8 702 
16 401 
22 294 
Table 3. The parameters of SRM 
3.3 Accuracy Assessment 
As shown in figure 3(g), we notice that the FNEA method 
always divides the big homogeneity region into lots of small 
regions with the similar size, especially highway. The limitation 
may be resolved by merging the same classes, but it is based on 
initial segmentation objects. When the scale becomes bigger, 
there still has the phenomena shown in figure 3(f). This is 
caused by the assumption that the objects with same scale have 
similar size which is in consistent with nature phenomena. As 
we know, building, road, grass and woodland belong to the 
same level of land cover class, however, their sizes are different 
greatly. 
We also notice that there have small redundant objects shown in 
figure 3(j). The redundant objects may be wiped off when the 
scale becomes large, but there remains the limitation all the 
same shown in figure 3(1). 
However, the new proposed method overcomes these 
limitations by SRM acting as initial segmentation. As shown in 
figure 3(d), we notice that the highway is integrally detached 
avoiding of smash objects. When the scale becomes bigger, 
some classes such as highway, building, grass are easy to 
extract, and there is less small objects shown in figure 3(f). The 
bigger the scale is, the fewer the object numbers are, the bigger 
the object region is, and the boundary of region may disappear 
or remain. 
Obviously, the new multi-scale segmentation method shows 
advantages over traditional multi-scale SRM and FNEA 
algorithm in the following aspects: 
(1) It makes full use of shape, spectral, scale, local, global 
information and the parameters could adjust for various 
classes. 
(2) It integrates the superiorities of SRM segmentation 
method and MHR merging method, and avoids the 
disadvantages of these methods. 
(3) The segmentation result described both in vector and 
raster format integrates RS and GIS expediently, and 
establishes better foundation for higher level image 
processing such as pattern recognition and automatic 
image interpretation.