) the best 
voi 
1000 1500 
road (red 
1, but the 
(sts a 9x9 
Euclidean 
chs, having 
method, a 
llelepiped) 
ecognition. 
rived from 
x-classifier 
ium values 
>d a binary 
[he binary 
| the next 
, Where the 
the shorter 
:d by these 
. parameter 
> computed 
ectangle 
During the 
th random 
arameter w 
the binary 
ned for this 
oad pixels 
e calculating ratio of the road and non-road pixels 
divided by the rectangle's area, weighted by the 
reciprocal logarithm of the road pixels. 
The genetic algorithm technology needs a suitable fitness 
definition, but all of the above mentioned have practical 
considerations. The first fitness optimization is a maximum 
search, but globally all road pixels can be categorized also by 
small rectangles (short road segments) The second fitness 
definition fixes this problem by a length/area based weighting, 
but behaves too rough and converges drastically into a single 
rectangle state. The last definition was found as the best (Eq. 5). 
  
logl# mask pixels) 
#roadpixels | 1 (5 
#maskpixels ) 
fitness= | = 
where the # operator means the number of pixels. The logarithm 
can be also the natural logarithm function. This fitness function 
supports the minimum search optimization. 
Other, similarly suitable fitness functions can be defined and 
their performance has to be evaluated. 
The listed fitness functions can be implemented not only 
considering genetic algorithms, but with differential evolution, 
too. 
The execution of the automatic road segment compilation must 
be followed by a human evaluation and network creation. 
4. RESULTS 
4.1 Segmentation results 
The support vector machine training results a support vector set 
of 1701 elements. The training lasts 140 s, thereafter the 
classification of the whole image requires only 56 s. The in- 
sample confusion matrix is as follows: 
  
  
  
  
  
  
  
SVM Classified 
Road Non-road| 2 OE PA 
$ [Read 1146 0|1146| 0,00%| 100,00% 
F |Non-road 142 481| 623|22,79%| 77,21% 
3 1288 481] 1769 
CE 11,02% 0,00% 91,9796| 88,6096 
UA 88,98%| 100,00% OA AA 
  
  
  
  
  
  
  
  
  
Table 1. In-sample accuracy for SVM classifier 
The overall accuracy is 91.9796, the average accuracy is 
88.60%, the accuracy of road recognition (producer's accuracy) 
is 100%. The output image is visually not satisfactory, because 
a lot of roof pixels are also ordered to the road category. 
The SOM solution has produced a label image containing 9 
different label values. The most similar label images are the sh 
and gh so their union is handled as road category. The 
confusion matrix was derived in the same way (Table 2). 
As one can see, the overall accuracy is less, than before: 
89.77%, the average accuracy is similarly lower: 85.55%. 
The hyperbox method was the last classification algorithm, 
which was also evaluated. This technique is very fast, because 
the relational operations are of very low level, so they can be 
executed quickly. The error matrix is in Table 3. 
The average accuracy is the highest at this method, it is 91.97%, 
the overall accuracy is very close to this value with 91.63%. 
The reason is the highest producer’s accuracy that means this 
method could recognize the non-road pixels with the highest 
rate. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
SOM Classified i OE PA 
Road Non-road OE PA 
+ |Road 1144 2|1146| 0,17%| 99,83% 
s Non-road 179 444| 623| 28,73% | 71,27% 
2 1323 4461 1769 
CE 13,53% 0,45% 89,77% | 85,55% 
UA 86,47% 99,55% OA AA 
Table 2. In-sample accuracy for SOM classifier 
Box-m Classified I OE PA 
Road Non-road OE PA 
+ |Road 1041 105| 1146| 9,16%| 90,84% 
Non-road 43 580| 623| 6,90%| 93,10% 
2 1084 685] 1769 
CE 3,97% 15,33% 91,63%| 91,97% 
UA 96,03% 84,67% OA AA 
  
  
  
  
  
  
  
  
  
Table 3. In-sample accuracy for hyperbox classifier 
The best binary result (Fig. 4) of the hyperbox algorithm 
therefore was propagated into the next phase. 
Hyperbox classification 
  
Figure 4. Result of the segmentation by the hyperbox technique 
Since the segmentation results were not fully satisfactory, and 
the genetic algorithm was intended to be tested, a set of 
synthetic images were created. These synthetic images contain 
only binary pixels labeling the road pixels. 
4.0 Road detection results 
In the detection phase two genetic technique were applied, 
firstly a general genetic algorithm (GA), then the differential 
evolution (DE). 
The GA tests (11 in total) were run with rectangle width of 7 
pixels. The mutation operation can be controlled by its buffer;