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Received：10 September 2018，
Published：07 July 2020
移动端阅览
道路信息在多个应用领域中发挥着基础性的作用。光学遥感影像能够以较高的空间分辨率对目标地物进行精细化解译，可大幅增强地物目标的提取能力。充分利用光学遥感影像丰富的几何纹理信息，进行道路的精确提取，已成为当前遥感学界研究的热点与前沿问题。鉴于此，本文依据近年来大量相关文献，对现有的理论与方法进行了归类与总结，通过分析不同方法采用的道路特征组合，将道路提取方法划分为模板匹配、知识驱动、面向对象和深度学习4类方法，简要介绍了道路提取普适性的评价指标并对部分方法进行了分析与评价；最后对现有光学遥感影像道路提取的发展提出了建议和展望。
Road extraction from remote sensing image has important practical value. It presents a major issue in the field of remote sensing. In recent years
the geometric texture of the target objects of aviation and satellite optical images has been refined in the wake of the rapid development of aviation technology. Thus
this technology provides a sufficient basis for automatic extraction of road information. However
full automation of road extraction through existing methods is difficult. In view of this issue
this study collects and synthesizes the existing methods on the basis of a large number of related literature in recent years. Ultimately
these road extraction methods are divided into four categories: template matching
knowledge-driven
object-oriented
and deep learning.
Template matching methods can be generally divided into rule template and variable template according to template type. The difference between the two types is whether the template can be drawn with regular graphics. Template matching mainly consists of the following three steps: template design
measure analysis
and location update. The template design can usually be set manually or by certain rules. Then
the target template is given in the measure analysis. Furthermore
the extreme value of the region is found by the measure function within the defined area. Lastly
the location of the road centerline is dynamically updated.
Roads
as artificial ruled features
provide large information. Hence
knowledge-driven methods based on relevant knowledge are used in road extraction work. On the basis of the relationship between knowledge and road
this study divides the knowledge-driven methods by three kinds: geometric knowledge
context knowledge
and auxiliary knowledge. Three methods are described as follows: (1) Geometric knowledge. The model is mainly constructed on the basis of the geometric features of the road. (2) Contextual knowledge. The method utilizes road-related auxiliary knowledge (motor vehicles
trees
zebra crossings
et al.) to assist in identifying the road. (3) Assisting knowledge. Road extraction is guided by using multisource remote sensing data
vector data
GPS data
navigation data
and public source data.
With the rapid increase of spatial resolution of optical remote sensing images
object-oriented methods have gradually become one of key methods in road extraction. First
the method selects the spectral and geometric feature criteria to segment image. Second
the geometric radiation similarity criterionis used to classify the segmentation region. Finally
the road extraction results are released by post-processing methods
such as mathematical morphology
matching tracking
and tensor voting.
Road extraction research belongs to the problem of remote sensing image interpretation
whereas deep learning supplies a new opportunity for satellite image interpretation in a semantic direction. Deep learning extracts road information with high precision through convolution
pooling
and training. Nevertheless
road breakage and mistaken extraction problems still exist.
At the end of the study
the direction of development of road extraction is envisioned. Two main trends are proposed: (1) a multimethod complementary road extraction system and (2) deep integration of deep learning and traditional methods. Thus
the detection area should be studied further.
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