首页 >  , Vol. , Issue () : 1993-2002

摘要

引用本文:

DOI:

10.11834/jrs.20208360

收稿日期:

2018-09-05

修改日期:

2019-06-27

PDF Free   EndNote   BibTeX
光学遥感影像道路提取的方法综述与展望
摘要:

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。有鉴于此,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四类方法,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。

Development and prospect of road extraction method for optical remote sensing image
Abstract:

Road extraction from remote sensing image has important application value, and it is also a hot issue in the field of remote sensing. In recent years, the geometric texture of the target objects of aviation and satellite optical images is more refined in the wake of the rapid development of aviation technology, which provides a sufficient basis for automatic extraction of road information. However, it is still difficult to fully automate road extraction by existing methods. In view of this, this paper collects and synthesizes the existing methods based on a large number of related literatures 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: (1) template design. The template can usually be set manually or by certain rules; (2) measure analysis. The target template is given. Furthermore, the region extreme value is found by the measure function within the defined area; (3) location updating, that is to say, location of road centerline is dynamically updated. Road, as an artificial ruled feature, has a large amount of relevant knowledge. Hence, knowledge-driven methods based on relevant knowledge are used to road extraction work. According to the relationship between knowledge and road, this paper divides the knowledge-driven methods in three kinds: geometric knowledge, context knowledge and auxiliary knowledge. Three methods are respectively described as follows: (1) Geometric knowledge. The model is mainly constructed based on the geometric features of road; (2) contextual knowledge. The method utilizes auxiliary knowledge (motor vehicles, trees, zebra crossings, etc.) related to road to assist in identifying the road; and (3) assisting knowledge. The road extraction is guided by using multi-source 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. Firstly, the method selects the spectral and geometric feature criteria to segment image. Secondly, it uses the geometric radiation similarity criterion to classify the segmentation region. Finally, the road extraction results are output 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, while the deep learning supplies a new opportunity to satellite image interpretation in semantic direction. Deep learning extracts road information with high precision through the procedure of convolution, pooling, and training. Nevertheless, there still has problems of road breakage and mistaken extraction. At the end of this paper, we prospect the direction of development of road extraction and propose two main trends: (1) multi-method complementary road extraction system; (2) deep integration of deep learning and traditional methods. Thus, the detection area should be studied further.

本文暂时没有被引用!

欢迎关注学报微信

遥感学报交流群 分享按钮