Road extraction by incremental Markov random field segmentation from high spatial resolution remote sensing images

doi:10.6046/gtzyyg.2018.03.11

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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.

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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	Ye LYU
	Xiangyun HU

Cite this article:

Ye LYU,Xiangyun HU. Road extraction by incremental Markov random field segmentation from high spatial resolution remote sensing images[J]. Remote Sensing for Land & Resources, 2018, 30(3): 76-82.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.11 OR https://www.gtzyyg.com/EN/Y2018/V30/I3/76

Fig.1 Validate area of road extraction

Fig.2 Sketch map of road incremental extraction principal

Fig.3 Road incremental extraction process

Fig.4 Initial setting

Fig.5 Incremental segmentation process

Fig.6 Example of incremental road extraction from rural area and highway in panchromatic image

Fig.7 Evaluation of experimental results

Tab.1 Evaluation of road extraction result

Fig.8 Road extraction result from a more complex scene

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