Road extraction in remote sensing images based on PCNN and mathematical morphology

The extraction of roads from high spatial resolution remote sensing images remains a problem though lots of efforts have been made in this area. High spatial resolution remote sensing images represent the surface of the earth in detail. As spatial resolution increases, spectral variability within the road cover units becomes complex and traditional remote sensing image processing methods on pixel basis are no longer suitable. This paper studies automatic road extraction from remote sensing images based on methods of Pulse-Coupled Neural Network and mathematical morphology. PCNN is a useful biologically inspired algorithm, and has the properties of linking field and dynamic threshold which make similar neurons generate pulses simultaneously. PCNN has the ability of a neuron to capture neighboring neurons which are in similar states and the independency of the pulses within unattached neuron regions. The method of mathematical morphology has the prime principle which is using a certain structure element to measure and extract the corresponding form in an image. In this paper, the simplified PCNN is applied as the image segmentation algorithm, and morphological transformation is used to purify the roads' information and to extract the road centerlines. Experimental results show that this method is efficient in road extraction from remote sensing images. © 2009 SPIE.