A cognitive framework for road detection from high-resolution satellite images

被引：8

作者：

Chandra, Naveen ^{[1
]}

Ghosh, Jayanta Kumar ^{[1
]}

Sharma, Ashu ^{[1
]}

机构：

[1] IIT Roorkee, Civil Engn Dept, Geomat Engn Grp, Roorkee, Uttar Pradesh, India

来源：

GEOCARTO INTERNATIONAL | 2019年 / 34卷 / 08期

关键词：

Cognitive; knowledge; support vector machine; AUTOMATIC DETECTION; EXTRACTION;

D O I：

10.1080/10106049.2018.1450451

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Road network extraction from high-resolution satellite (HRS) imagery is a complex task. It is an important field of research and is widely used in various cartographic applications such as updating and generating maps. The objective of this research work is to develop a novel framework, emulating human cognition, for detection of roads from HRS images. Roads network from HRS images are detected using support vector machines within the different stages of cognitive task analysis. In the first stage, basic information about the cognitive parameters which are required for image interpretation is collected. In the second stage, the rule-based method is used for knowledge representation. Lastly, during knowledge elicitation, the developed rules are used to extract roads from HRS images. The proposed method is validated using 16 HRS images of developed suburban, developed urban, emerging suburban and emerging urban region.

引用

页码：909 / 924

页数：16

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