The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B3b. Beijing 2008 
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image are calculated and recorded in the Table 1. The expected 
of the road’s gray value is much lower than that of the 
background in despite of the speckle noise effect, and it is 
always below 80. Meanwhile, the variance of the road is also 
much more lower than that of the background. Thus, another 
conclusion can be made that the road area is more homogenous 
than its background. 
statistical property we discussed in the previous section . An 
almost automatic algorithm is devised with the assumptions that: 
1 ) There is obvious contrast between road and its background; 2) 
The road itself is homogenous; 3) Road area is more 
homogenous than its neighborhood when taking the road 
candidate into account. 
According to the first assumption, the local structure model of 
the road area is formed by three paralleling parts shown in 
figure 3 from which we can see that B represents the road area 
whose center is the target pixel while A and C represents the 
surrounding background area(Touzi, 1989; Geling, 1993). By 
computing the summation of the pixel gray value of each area 
and the average ratio of the road area to the background area 
according to formula (1) 
(b) Vertical direction 
Figure 2. Relation of the gray value sum and coordinate 
Road 
1 
2 
3 
4 
5 
E 
30.6 
54.52 
13.12 
19.24 
41.16 
V 
6.29 
10.25 
3.22 
4.54 
7.68 
Non 
1 
2 
3 
4 
5 
E 
173.8 
134.9 
195.2 
107.7 
154.1 
4 
6 
4 
2 
2 
V 
32.77 
24.94 
35.88 
20.29 
28.73 
X 
/= 1 
m 
R 2 
X B, 
(i) 
we can get Ri and R 2 . If R]<1 or R 2 <1, then the central pixel of 
area B is a road candidate point. This operator can be used in 4 
or 8 directions, the minimum value is chosen to be the best 
response. The first operator replaces the target pixel’s own gray 
value with its neighborhood gray value and thus controls the 
speckle noise influence to the road extraction effectively. 
While on the other hand, the replacement of target pixel’s gray 
value by its neighborhood’s will certainly lead to wrong 
detection, regarding the non-road pixel along the road edge as 
road candidate which also lead to the road fracture. In the 
previous part of this article, we discussed the statistical property 
of the road in SAR images, and concluded that the road area is 
homogenous in SAR images and is more homogenous than the 
background area in the meantime. In order to solve the previous 
problem, a statistic is introduced to describe the homogeneity of 
the imagery. Chose a 3X3 or 5X5 window with the target 
pixel at the center and calculate the variance(D) of this area, 
then we can get R3 by formula (2). 
Table 1. The expectation and variance of the road and 
non road area(E: expectation, V: variance) 
3. ROAD LINEAR FEATURE DETECTOR 
In order to reduce the influence of the multiplicative speckle, 
the method proposed in this paper is based on the gray 
«3 = 
(2) 
As there are some homogenous areas in parts of the background, 
we take the pixel gray value(P) into consideration and get R4 
by formula (3).