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Road Network Detection Using Probabilistic and Graph Theoretical Methods
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AI
DOI：10.1109/TGRS.2012.2190078.png)
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Abstract
En 中文
Road network detection from very high resolution satellite and aerial images has diverse and important usage areas such as map generation and updating. Although an expert can label road pixels in a given image, this operation is prone to errors and quite time consuming. Therefore, an automated system is needed to detect the road network in a given satellite or aerial image in a robust manner. In this paper, we propose such a novel system. Our system has three main modules: probabilistic road center detection, road shape extraction, and graph-theory-based road network formation. These modules may be used sequentially or interchangeably depending on the application at hand. To show the strengths and weaknesses of our system, we tested it on several very high resolution satellite (Geoeye, Ikonos, and QuickBird) and aerial image sets. We compared our system with the ones existing in the literature. We also tested the sensitivity of our system to different parameter values. Obtained results indicate that our system can be used in detecting the road network on such images in a reliable and fast manner.
Keywords:
Aerial images
binary balloon algorithm
edge detection
graph representation
kernel-based density estimation
road network detection
satellite images
Journal
IF:
8.6
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2.2W
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10.7W
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