Paper Info
Title
Road extraction from high-resolution remotely sensed panchromatic image in different research scales
Abstract
As a main factor of evaluating city development speed, road is one of the fast information updating element during city development. Road information extraction based on high-resolution remotely sensed images has very important significance because road affects city land use-cover change. Based on the synthetically analyzing all kinds of road extraction method from high-resolution remotely sensed images, this paper presents the road extraction method that firstly extracts parcel-units from the experimental image based on different scales, and then analyzes the parcel-units assisted by transcendental knowledge. This method can extract road information exactly by object-oriented method, and can acquire different-scale roads according to the user's requirement. At last this paper gives the road information extraction result analysis of the experiment sample.
Year
DOI
Venue
2010
10.1109/IGARSS.2010.5649912
IGARSS
Keywords
Field
DocType
information updating element,terrain mapping,road information extraction,parcel-unit,scale,image resolution,roads,target recognition,panchromatic image,high-resolution remotely sensed panchromatic image,feature extraction,geophysical image processing,city development,road extraction,user requirement,different-scale road,object-oriented method,formal specification,transcendental knowledge,city land use-cover change,object-oriented methods,information extraction,remote sensing,classification algorithms,data mining,image segmentation,high resolution
Computer vision,Panchromatic film,Computer science,Remote sensing,Formal specification,Image segmentation,Feature extraction,Information extraction,Artificial intelligence,Statistical classification,Image resolution,User requirements document
Conference
ISSN
ISBN
Citations 
2153-6996 E-ISBN : 978-1-4244-9564-1
978-1-4244-9564-1
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Zhanfeng Shen16812.60
Jian-Cheng Luo29920.75
Lijing Gao311.36