Segmentation of satellite imagery based on pulse-coupled neural network
Vegetation encroachment under overhead high voltage power lines and its monitoring is a challenging problem for electricity distribution companies. Blackout can occurs if proper monitoring of vegetation is not done. The uninterrupted electric power supply is vital for industries, businesses, and dai...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE Computer Society
2015
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962622509&doi=10.1109%2fIconSpace.2015.7283835&partnerID=40&md5=c5e7aa1ace38cd45a2e64f286c8a987c http://eprints.utp.edu.my/26204/ |
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| Summary: | Vegetation encroachment under overhead high voltage power lines and its monitoring is a challenging problem for electricity distribution companies. Blackout can occurs if proper monitoring of vegetation is not done. The uninterrupted electric power supply is vital for industries, businesses, and daily life. Therefore, it is mandatory for electricity companies to monitor the vegetation/trees near power lines to avoid the blackouts. Pulse-coupled neural network (PCNN) considered as differently from converntial neural networks used in many signal and image processing applications. The main step to develop the automatic detection of vegetation is performing an image segmentation which is normally used to identify or marking of vegetation from the acquired images. We apply PCNN for image segmentation on satellite images for vegetation monitoring purposes and compared the performance with a thresholding image segmentation method with Pulse coupled neural network. The results show that PCNN produce outperform as compared to the thresholding method in terms of detection accuracy. © 2015 IEEE. |
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