eration 
evolution 
ramely the 
ay that they 
population 
at negative 
f the genes. 
ulation, the 
ing can be 
diagram. À 
0 
bling 
)ptimization 
^" If one has 
produced the final population by DE in the studied image, a 
strong gene multiplication can be diagnosed as in Fig. 9. A 
skilled human operator can extract the right candidates from the 
resulting set, passing them into the last phase. 
Final population 
  
Figure 9. Final population from differential evolution 
The last experiment is a comparison with the artificial images to 
obtain recognition features of the technique. The synthetic 
images were processed by the same DE settings and the result is 
self-explainable (Fig. 10). 
5. CONCLUSION 
The proposed neural and genetic solution is based on pure 
optical image information. There was no additional help in the 
segmentation to extract the road pixels. Being able to execute 
this step on a highly reliable level, the next step can obtain 
better fitting performance. The original assumption was that the 
widely used modern neural techniques, like SVM and SOM can 
bring excellent result in road recognition. The first experiment 
series aimed to check this hypothesis. The results were 
surprising, because the hyperbox method could reach better 
scores in this test. The additional image based information band 
(like the NDVI) and the principal component analysis could 
increase the accuracy. Object oriented segmentation or more 
sophisticated road detection methodologies producing pixel- 
type result can replace the presented methods. 
The biggest novelty is the application of the genetic algorithms 
in information retrieval from segmented images. The common 
genetic algorithm has a quite long run until a stable state can be 
reached, the new differential evolution technique can replace it 
because of its higher speed. The shown GA and DE methods 
have global optimization feature; the dependence from the 
implemented fitness function is crucial. 
  
Figure 10. Synthetic segmentation image processed by DE 
algorithm (longest 5 of the 10 best genes) 
More research must focus on the suitable local applicable 
fitness function, which is able to compile the pixels into road 
segments, but is fast and reliable at the same time. The genetic 
solution is almost independent from the image size and 
resolution, as well as from the number of road elements 
(segments), when enough genes are handled in the population. 
The experiment has proven that based on mutation this 
algorithm can extract such linear image elements. 
The used software environment was Mathworks Matlab, which 
is an interpreter type environment. A great experience was with 
the differential evolution technique that it was suitable at full 
resolution to bring acceptable results. A future development by 
e.g. OpenCV can dramatically increase the size of the image to 
be processed. 
6. REFERENCES 
Beale, M.H., Hagan, M.T., Demuth, HB., 2012. Neural 
Network Toolbox. User’s Guide, R2012a, Matlab, The 
MathWorks Inc, Natick 
Laky, S., 2012. Metaheuristic optimization in the geodesy, PhD 
thesis, Budapest, p. 115 
Rottensteiner, F., Baillard, C., Sohn, G., Gerke, M., 2011. 
ISPRS Test Project on Urban Classification and 3D Building 
Reconstruction, ISPRS - Commission III — Photogrammetric 
Computer Vision and Image Analysis, Working Group III / 4 - 
Complex Scene Analysis and 3D Reconstruction. 
http://www.commission3.isprs.org/wg4/ 
7. ACKNOWLEDGEMENT 
This work is connected to the scientific program of the 
"Development of quality-oriented and harmonized R+D+I 
strategy and functional model at BME" project. This project is 
supported by the New Hungary Development Plan (Project ID: 
TAMOP-4.2.1/B-09/1/KMR-2010-0002)