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Road extraction by incremental Markov random field segmentation from high spatial resolution remote sensing images |
Ye LYU, Xiangyun HU |
School of Remote Sensing and Engineering, Wuhan University, Wuhan 430072, China |
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Abstract Remote sensing technology has been the most effective and efficient method for extracting information from the earth surface. Roads in the high spatial resolution remote sensing images are areas with very complex road features. None road objects, such as cars, lanes and pedestrians, will change road appearance greatly, which makes road extraction difficult. The authors take advantage of Gaussian mixture model and Markov random field, which adapts to interference, to evaluate foreground and background models and label their pixels. As roads go through the remote sensing images, the areas far from the roads are useless for road extraction, and hence local incremental segmentation method takes effect. The experiments show that methods used in this paper are fairly effective.
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Keywords
Markov random field
incremental segmentation
road extraction
remote sensing images
high spatial resolution
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Issue Date: 10 September 2018
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