道路通行障碍物遥感检测与通过性评价

doi:10.6046/gtzyyg.2020.02.13

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摘要

针对现有道路障碍物遥感检测方法对配准精度要求高、受成像条件影响大、自动化程度不高以及需要专业人员操作等不足,提出了一种基于反向特征匹配的道路障碍物检测方法和路段通过性评估方法。该方法基于尺度不变特征变换(scale-invariant feature transform,SIFT)特征提取算法,通过获取灾后影像上道路缓冲区内不能匹配的特征点集来检测定位障碍物,然后采用多种子点区域生长算法获得障碍物分布范围与形状,最后通过与道路矢量数据叠置分析进行路段通过性评价。实验结果表明,该方法能有效地提取因自然灾害而产生的山区公路障碍物位置和面积信息。

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	康晋洁
	戚浩平
	杨清华
	陈华

关键词 ：高分辨率遥感影像, 障碍物检测, SIFT, 区域生长, 通过性

Abstract：

In view of the defects of the existing road obstacle detection methods, such as requirement for high registration accuracy, influence by imaging conditions , low-level automation and the need for professional operation, this paper proposes a road obstacle detection method based on reverse feature matching and accessibility evaluation method. On the basis of SIFT feature extraction algorithm, this method detects obstacles by acquiring the set of feature points that are not matched in the road buffer area of the disaster image, then obtains the distribution range and shape of obstacles by using a variety of sub-point region growing algorithms, and finally evaluates the road accessibility by overlapping analysis with road vector data. The experimental results show that this method can effectively extract the position information and shape information of obstacles.

Key words： high-resolution remote sensing image obstacle detection SIFT region growth accessibility

收稿日期: 2019-06-28 出版日期: 2020-06-18

TP79

基金资助:高分辨率对地观测系统重大专项项目“**重点区遥感综合调查及关键技术与标准研究”资助(1204-120402)

通讯作者: 戚浩平

作者简介: 康晋洁(1995-),女,硕士研究生,主要研究方向为遥感影像目标提取与分析。Email: 317540589@qq.com。

引用本文:

康晋洁, 戚浩平, 杨清华, 陈华. 道路通行障碍物遥感检测与通过性评价[J]. 国土资源遥感, 2020, 32(2): 94-102.
Jinjie KANG, Haoping QI, Qinghua YANG, Hua CHEN. Study of remote sensing detection method for road obstacle and accessibility evaluation. Remote Sensing for Land & Resources, 2020, 32(2): 94-102.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.02.13 或 https://www.gtzyyg.com/CN/Y2020/V32/I2/94

Fig.1 影像对比度对特征点检测的影响

Fig.2 障碍物特征点筛选示意图

Fig.3 障碍物特征点分组

Fig.4 相同障碍物两侧特征点识别归并

Fig.5 提取标准道路样本

Fig.6 标定障碍物候选点性质

Tab.1 障碍物候选点的性质

Tab.2 通过性评价

Fig.7 灾后影像和障碍物分布

Fig.8 道路ROI内特征点

Fig.9 障碍物特征点集示例

Fig.10 障碍物候选点示例

Tab.3 候选点性质的标定结果

Fig.11 障碍物检测结果

Tab.4 障碍物提取结果

Fig.12 叠置分析

Fig.13 道路ROI内特征点

Fig.14 障碍物特征点筛选及分组

Fig.15 障碍物检测结果

Tab.5 障碍物的位置和面积信息

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