Automatic road extraction using seeded region growing with mixed art method for DSM data

In this paper, we introduce another approach for road extraction from Digital Surface Model (DSM) Data. DSM Data is based on elevation of the surface, and the benefit of using the DSM data is to avoid the problem that caused by shadow of the building, trees and so on. For road extraction, we use a fundamental technique using segmentation processing. First, we employ the Adaptive Resonance Theory (ART) Model; we use Fuzzy ART and Symmetric Fuzzy ART (S Fuzzy ART) method, the unsupervised learning for analog patterns. However, this method should be followed a labeling process to separate the same cluster but in the different region. Therefore, this method requires a relatively long processing time. The second approach for segmentation uses the region growing method based on a similarity criterion. A threshold should be provided to measure the homogeneous of the region with the adjacent. However, to determine a threshold is not easy. In this paper, we proposed a Mixed ART that combines the Fuzzy ART and S Fuzzy ART method. Furthermore, we compromise the Mixed ART method and the Region Growing method to improve the performance. This method uses the Region Growing for segmentation process and uses the resonance approach for homogeneity measurement. The advantage of using the Region Growing method, we could control the seed point to achieve a satisfactory performance for extracting the road. The experimental result shows that the proposed method increases the performance up to four times faster without sacrificing the quality.