The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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characteristics of these road network structures, roads in USA 
tend to be parallel and cross each other orthogonally forming T- 
junctions or crossroads, whereas European roads tend to wiggle 
and meet or cross each other at roundabouts. Thus it seems 
natural that Hp < 2 bits are necessary to encode information 
about road segments at junctions for road networks in the USA, 
whereas for road networks in Europe, Hp> 2 bits are necessary. 
The same measure can also be used to distinguish between 
Mountains and Fields, while the ‘density’ features distinguish 
rural networks from urban networks. 
A ‘distribution’ measure of edges at a vertex provides us with 
information as to how the edges at a vertex are distributed in the 
network. Let E D i be the proportion of junction points with i 
edges at them. We use mean(£^ fi ) and var(£ D l ) as features. The 
variance of the edge distribution is lower in the case of 
networks in urban areas as opposed to rural, and it is lower also 
in the case of urban networks in the USA as opposed to in 
Europe. 
Notation 
Description 
N, 
Junction density 
L 
Network length 
L 
Length density 
Ä 
Network area density 
Pe 
Ratio of length 
var(p) 
Ratio of lengths variance 
mean(p) 
Ratio of lengths mean 
K 
Average curvature 
var(k) 
Average curvature variance 
mean(k) 
Average curvature mean 
Ep, 
Number of junction with m v =i 
var(E Dli ) 
Edge distribution variance 
mean(E D ,i) 
Edge distribution mean 
Ej 
Number of junction edges 
Ej 
Junction edges density 
Mj, a 
Density of junction edges per quadrant 
var( Mj) 
Junction edges density variance 
mean( Mj) 
Junction edges density mean 
K, 
Local junction density 
var(N ri ) 
Variance of the local junction densities 
mean(N r ,) 
Mean of the local junction densities 
ßj 
Vectors of angles between segments at junction 
j 
Jk 
Entropy of road segment orientation 
Table 1: Summary of the features computed from road networks 
2.3 Urban Region Features 
We focus on the last four features in Table 2. These features 
enable us to distinguish between rural classes (Villages and 
Fields) and urban class (Europe), which otherwise were 
misclassified due to the lack of extracted network information 
from the small compact urban regions in the images, shown in 
Figure 3(a) and Figure 3(c). Let £2 and Q R be the area of the 
image and the area of the extracted regions respectively and L\|/ 
and be the network length in ¥ = £2 - Q R and perimeter of 
the extracted regions respectively. 
We define two descriptors, R A , the extracted region density and 
Cf A = fV'r/ the extracted region compactness factor. These 
two features help us to distinguish the Villages class from the 
rest of the classes: for example, R A ~ 1 for Urban classes and R A 
~ 0 for Mountains and Fields classes. 
The number of urban regions in an image, the feature R v , is used 
to distinguish between complete Urban, Villages, Fields and 
Mountains. A complete Urban (USA and Europe) will have R v 
= 1, whereas, a Villages will have R v >1, and Fields and 
Mountains will have R v = 0. Another feature A n = Cl R / L\|/, the 
inverse fractional length density, is also computed to separate 
the Village class from Urban and Mountains and Fields. For 
complete Urban classes (USA and Europe), L\j/ = 0, and for 
Mountains and Field classes = £2. Hence for Mountains and 
Fields classes, A n = 0, while for complete Urban classes, A n = oo, 
and for the Village class 0 < A n < oo.We augment these urban 
region features with the features computed from the graph 
representation of the road network as described earlier to 
improve the classification of the geographical environments 
which otherwise were misclassified due to the loss of 
information from small dense urban regions. 
Notation 
Description 
£2 
Area of image 
Q r 
Area of extracted regions 
Ly 
Network length in 'F = £2 - £2 Ä 
r* 
Perimeter of extracted regions 
Ra 
Region area density : £2 fi /£2 
Cf A 
Region compactness factor T R 2 / £2* 
R v 
Number of regions : # R 
Inverse fractional length density : £2 R /Ly 
Table 2: Summary of features computed for urban areas. 
3. CLASSIFICATION 
The 32 features (16 features for each network extraction 
method) described in section 2.2 were computed for a database 
of 497 SPOT5, 5m resolution images. To provide ground truth, 
these images were manually classified into the 7 classes 
described in section 1 representing various kinds of urban and 
rural environments. Machine classification was done with a 
five-fold cross validation on the data set, with 80% of data for 
training and the remaining 20% for testing in each fold. We 
performed feature selection using a Fisher Linear Discriminant 
(FLD) analysis (Duda et al., 2000), followed by a SVM linear 
kernel classification on the selected feature set. The result of the 
classification is shown in Table 3. The SVM linear kernel 
classification on the 30-dimensional feature space selected by 
the FLD shows a mean error of 24.5% with a standard deviation 
of 2.92%. As can be clearly seen in the confusion matrix Table 
3, the Villages class is confused with the Fields class and also 
there is a slight confusion between the Urban USA and Urban 
Europe classes. These confusions arise because, as stated above, 
the road extraction methods fail to detect the fine and densely 
structured roads present in some images. Table 4 shows the 
results of classification of the same set of images with 20 
selected feature out of 36 features (32 road network features 
plus the 4 features computed from the segmented urban areas). 
As can be seen, there is an improvement in the confusion matrix. 
The Villages class is less confused with the Fields class than 
before. The SVM linear kernel classification error is drastically 
reduced from 24.5%, with only road network features to 12.9%, 
with the combined feature set with a standard deviation of