利用增量式马尔科夫随机场分割提取高空间分辨率遥感影像道路

doi:10.6046/gtzyyg.2018.03.11

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摘要

利用遥感影像进行道路提取,能够及时提供道路更新数据。高空间分辨率遥感影像中的道路成面状,且具有复杂的道路特征。其中,车流、道路线和行人等其他非道路因素的干扰会使道路的特征变化变得复杂,对道路提取造成困难。为此,利用高斯混合模型与马尔科夫随机场模型进行前景、背景模型估计与路面区域分割,以克服路面干扰因素的影响。由于道路贯穿于遥感影像,远离道路区域的影像对道路提取并无用处,故利用局部增量式分割方法确定道路提取有效区域,在其内部进行更精确的路面提取。实验结果表明,该方法效果明显有效。

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	吕野
	胡翔云

关键词 ：马尔科夫随机场, 增量式分割, 道路提取, 遥感影像, 高空间分辨率

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.

Key words： Markov random field incremental segmentation road extraction remote sensing images high spatial resolution

收稿日期: 2017-03-02 出版日期: 2018-09-10

TP753

作者简介: 吕野(1991-),男,硕士研究生,主要研究方向为遥感影像目标提取与解译研究。Email: ye. lv@whu.edu.cn。

引用本文:

吕野, 胡翔云. 利用增量式马尔科夫随机场分割提取高空间分辨率遥感影像道路[J]. 国土资源遥感, 2018, 30(3): 76-82.
Ye LYU, Xiangyun HU. Road extraction by incremental Markov random field segmentation from high spatial resolution remote sensing images. Remote Sensing for Land & Resources, 2018, 30(3): 76-82.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.03.11 或 https://www.gtzyyg.com/CN/Y2018/V30/I3/76

Fig.1 道路提取有效区域示意图

Fig.2 道路增量提取原理示意图

Fig.3 道路增量式提取流程

Fig.4 初值设置

Fig.5 增量式分割提取流程

Fig.6 全色影像中乡村道路与公路道路增量式提取示例

Fig.7 实验结果评价

Tab.1 道路提取结果评估

Fig.8 一个更加复杂场景的高空间分辨率遥感影像道路提取结果

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