Road Centerline Vectorization by Self-organized Mapping

A novel approach to semi-automated road centerline extraction from remotely sensed imagery is introduced. Providing inspiration is Kohonen’s self-organizing map (SOM) algorithm. With IFOV < 2m, road features are open to regionbased analysis. A variation of the basic SOM algorithm is implemented in a region-based approach to road vectorization from high spatial (1.0m) and spectral resolution imagery. Using spectrally classified road pixels as input, centerline nodes are located via cluster analysis of the local density fluctuations in the input space. Linking the self-organized locations of the nodes with a minimum spanning tree algorithm provides global topological structure, which is subsequently refined. The idea is use contextual analysis from which to derive optimum topology. The result is a vectorized road centerline network suitable for direct GIS database population. Preliminary results demonstrate the algorithm’s potential for robust vectorization when presented with noisy input.