Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach |
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Authors: | A Mohammadzadeh M J Valadan Zoej A Tavakoli |
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Institution: | (1) Visualization and Perception Lab, Dept. of CSE, Indian Institute of Technology, Madras, Chennai, 600 036, India;(2) Dept. of Civil Engg, Indian Institute of Technology, Madras, Chennai, 600 036, India |
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Abstract: | Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation
of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to extract roads
from high resolution satellite images. In this study, using few samples from road surface, a particle swarm optimization is
applied to a fuzzy-based mean calculation system to obtain road mean values in each band of high resolution satellite colour
images. Then, the images are segmented using the calculated mean values from the fuzzy system. Optimizing the fuzzy cost function
by particle swarm optimization enables the fuzzy approach to be the best mean value of road with sub-grey level precision.
Initially, this method was applied to simulated images where the calculated mean values are consistent with the hypothetic
mean values. Application of the method to IKONOS satellite images has shown a prospective outcome for automatic road extraction.
Mathematical morphology is subsequently used to extract an initial main road centreline from the segmented image. Then, small
redundant segments are automatically removed. The quality of the extracted road centreline indicates the effectiveness of
the proposed approach. |
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Keywords: | |
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