ROAD DETECTION BY NEURAL AND GENETIC ALGORITHM IN URBAN 
ENVIRONMENT 
A. Barsi* 
Dept. of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 
Budapest, Hungary — barsi@eik.bme.hu 
Commission III, WG III/4 
KEY WORDS: Segmentation, Genetic algorithm, Differential evolution, Road detection 
ABSTRACT: 
In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images 
about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and 
airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was 
ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a 
compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function 
and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The 
neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The 
compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution 
technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have 
proven that the evolutional technique can automatically find major road segments. 
1. INTRODUCTION 
The working group III/4 of ISPRS Commission III has 
conducted a challenge of detecting man-made objects from 
digital aerial imagery. These objects are urban objects (roads, 
trees, cars etc.) and 3D buildings. This challenge has been 
supported by adequate data sets: there was an aerial 
photography campaign near Stuttgart, Germany, when color 
images and LIDAR-point clouds were acquired. The raw data 
were processed: the aerotriangulation was calculated, and 
digital elevation model (DEM) was derived. All of these 
products can be downloaded and used for developing object 
detection methodologies. 
The author is strongly interested in the use of current 
developments of computer science in digital image 
understanding. Among the modern methods neural network 
based classification techniques and genetic algorithms can be 
mentioned, which are in the focus of the current paper. 
The general theory of the two artificial intelligence tools is 
presented, followed by the details of the applied methodology in 
image analysis. 
2. NEURAL AND GENETIC ALGORITHMS 
2.1 Neural networks as classifiers 
The artificial neural networks have become widely used tool in 
different data processing workflows, among them also in digital 
image processing. There are several types of network; therefore 
different categorization can be done, for example 
  
* Corresponding author. 
e by the architecture (layers, recurrent, delay, transfer 
functions etc.), 
e by the initialization, training, testing and validation 
algorithms, 
e by the data structure and configuration, 
e by the used pre- and post-processing operations. 
The most used neural networks in image processing have a 
strong focus on classification, which is appropriate application 
for back-propagation (BP) network, radial basis function (RBF) 
networks, learning vector quantization (LVQ) networks and 
support vector machines (SVM). Beside these supervised 
classifiers, the unsupervised category is similarly important: e.g. 
self-organizing maps (SOM), competitive learning networks. 
[Beale et al, 2012] 
The current paper presents applications of SVM and SOM 
technologies. The used support vector machine classification is 
based on the formula: 
c- 2 au x) (1) 
where s; is the support vector, x is the vector to be classified, 
k() is the kernel function, o; is a weight, b is a bias, and c is 
the result, which having a value more than zero means to be 
classified in the category, otherwise it is rejected. The applied 
kernel function was the radial basis function (RBF), which is a 
nonlinear function, so it has the advantage to make right 
decision even with complicated class borders. This supervised 
classification method must be parameterized by data from 
training areas [Beale et al, 2012]. 
    
  
   
  
  
   
  
   
  
  
  
  
  
  
  
  
  
  
  
  
  
  
    
    
   
   
   
   
    
   
   
   
   
   
    
   
      
    
  
  
  
  
     
  
    
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