一种高分辨率遥感影像道路提取方法

戴激光; 朱婷婷; 张依蕾; 马榕辰; 王晓桐; 张腾达

doi:10.16383/j.aas.c190534

一种高分辨率遥感影像道路提取方法

doi: 10.16383/j.aas.c190534

戴激光^{1, 2,},
朱婷婷^1,,
张依蕾^1,,
马榕辰^1,,
王晓桐^1,,
张腾达^1,

1.
辽宁工程技术大学测绘与地理科学学院阜新 123000
2.
城市空间信息工程北京市重点实验室北京 100038

基金项目: 国家自然科学基金项目(41871379), 自然资源部国土卫星遥感应用重点实验室经费资助项目(KLSMNR-202004), 辽宁省教育厅服务地方项目(LJ2019FL008), 城市空间信息工程北京市重点实验室(2020221), 地理国情监测国家测绘地理信息局重点实验室(2018NGCM01)资助

详细信息

作者简介:
戴激光：辽宁工程技术大学测绘与地理科学学院副教授. 2013年获得辽宁工程技术大学博士学位. 主要研究方向为遥感影像信息提取. 本文通信作者. E-mail: daijg03@163.com

朱婷婷：辽宁工程技术大学硕士研究生. 2017年获得辽宁工程技术大学学士学位. 主要研究方向为遥感影像道路提取. E-mail: tingting9295@163.com

张依蕾：辽宁工程技术大学硕士研究生. 2018年获得辽宁工程技术大学学士学位. 主要研究方向为矢量信息分析. E-mail: zhangyilei@lntu.edu.cn

马榕辰：辽宁工程技术大学硕士研究生. 2018年获得辽宁工程技术大学学士学位. 主要研究方向为遥感影像特征提取. E-mail: 471820526@stu.lntu.edu.cn

王晓桐：辽宁工程技术大学硕士研究生. 2018年获得辽宁工程技术大学学士学位. 主要研究方向为道路信息提取. E-mail: wangxiaotong@lntu.edu.cn

张腾达：辽宁工程技术大学硕士研究生. 2018年获得辽宁工程技术大学学士学位. 主要研究方向为遥感影像信息提取. E-mail: zhangtengda@lntu.edu.cn

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出版历程
- 收稿日期: 2019-07-18
- 录用日期: 2019-11-08
- 刊出日期: 2020-11-24

A Road Extraction Method for High Resolution Remote Sensing Images

1.
School of Geomatics, Liaoning Technical University, Fuxin 123000
2.
Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038

Funds: Supported by National Natural Science Foundation of China (41871379), Key Laboratory of Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China(KLSMNR-202004), Liaoning Provincial Education Department Serves Local Projects(LJ2019FL008), Beijing Key Laboratory of Urban Spatial Information Engineering (2020221), Key Laboratory for National Geograophy State Monitoring (National Administration of Surveying, Mapping and Geoinformation) (2018NGCM01)

摘要

摘要: 针对空间异质性导致的道路几何纹理特征突出性下降问题, 提出一种高分辨率遥感影像道路提取方法. 首先设定跟踪模型, 依据人工输入点, 自适应提取道路中心点和道路宽度, 设计迭代内插、双向迭代两种跟踪方式以及矩形跟踪模板; 然后提出多描述子道路匹配模型, 针对道路几何纹理特征突出性不足问题, 基于道路区域地物边缘与道路方向一致的语义关系, 通过线段峰值约束的思想, 提出一种多尺度线段方向直方图(Multi-scale line segment orientation histogram, MSLSOH)描述子, 以此对跟踪方向进行预测; 针对道路几何纹理特征均质性下降问题, 从道路区域与道路非道路混合区域纹理差异性出发, 组合三角形构成扇形描述子, 突出道路影像纹理特征, 以此不仅可对预测跟踪点进行验证, 而且也可在结构信息缺失的情况下对道路进行跟踪; 最后选取不同类型、不同分辨率、不同场景的高分辨率遥感影像, 通过与其他方法的实验对比, 表明该方法能够解决道路提取过程中几何纹理特征突出性下降问题, 具有准确率高和自动化程度高的优势.
- 语义关系 /
- MSLSOH描述子 /
- 扇形描述子 /
- 道路提取 /
- 高分辨率 /
- 遥感影像
Abstract: In order to solve the problem of road geometric texture feature prominence decline caused by spatial heterogeneity, a road extraction method for high resolution remote sensing images is proposed in this paper. This method first sets a tracking model, adaptively extracts road center point and road width according to manual input points, thereby designing two tracking methods (interpolation and bidirectional iteration), and a rectangular tracking template. Secondly, a multi-descriptor road matching model is proposed: Facing with the lack of prominent geometric features of roads, based on the semantic relationship between the edges of the objects in the road area and the direction of the road, a MSLSOH (Multi-scale line segment orientation histogram) descriptor is proposed to predict the matching tracking direction through the idea of line segment peak constraint. And then, for the problem of the homogeneity reduction of road texture features, from the difference of texture between road area and road non-road mixed area, a sector descriptor composed of multiple triangles was designed to highlighting the road image texture features, so that not only the predicted tracking points can be verified, but also tracking roads in the absence of structural information. Finally, high resolution remote sensing images of different types, resolutions and scenes are selected. Compared with other methods, the experimental results show that this method can solve the problem of decreasing the prominence of geometric texture features in the process of road extraction, and has the advantages of high accuracy and high degree of automation.
- Semantic association /
- MSLSOH descriptor /
- sector descriptor /
- road extraction /
- high resolution /
- remote sensing images

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图 1 技术流程

Fig. 1 The technical route

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图 2 模板参数自适应提取

Fig. 2 Adaptive extraction of template parameters

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图 3 跟踪方式

Fig. 3 Tracking mode

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图 4 矩形跟踪模板设定

Fig. 4 Rectangular tracking template setting

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图 5 MSLSOH描述子

Fig. 5 MSLSOH descriptor

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图 6 扇形描述子

Fig. 6 Sector descriptor

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图 7 梯度阈值分析

Fig. 7 Gradient threshold analysis

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图 8 峰值比例因子分析

Fig. 8 Peak scale factor analysis

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图 9 方差阈值分析

Fig. 9 Variance threshold analysis

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图 10 Geoeye-1影像道路中心线提取

Fig. 10 Road centerline extracted for Geoeye-1 image

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图 11 Pleiades影像道路中心线提取

Fig. 11 Road centerline extracted for Pleiades image

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图 12 高分2号影像道路中心线提取

Fig. 12 Road centerline extracted for GF-2 image

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表 1 不同道路提取方法对比

Table 1 Comparison of different methods for road extraction

评价参数	本文方法			Erdas 算法			T 型模板算法			圆型模板算法
评价参数	实验一	实验二	实验三	实验一	实验二	实验三	实验一	实验二	实验三	实验一	实验二	实验三
完整度 (%)	99.7	99.7	99.6	99.8	99.6	99.7	94.7	99.2	97.4	99.4	99.7	99.1
正确率 (%)	99.5	96.4	99.4	99.6	99.7	99.8	96.4	88.5	99.1	99.5	96.3	98.7
提取质量 (%)	99.2	96.0	99.1	99.4	99.3	99.5	90.8	87.9	96.5	98.9	96.0	97.8
种子点数 (个)	22	6	24	199	62	107	70	34	110	61	13	46
运行时间 (s)	348	102	374	803	259	457	416	152	553	279	116	231

下载: 导出CSV

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