NDLI: Fully automated road network extraction from high-resolution satellite multispectral imagery

Content Provider	IEEE Xplore Digital Library
Author	Shackelford, A.K. Davis, C.H.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Missouri-Columbia, Columbia, MO, USA (Shackelford, A.K.; Davis, C.H.)
Abstract	We present a fully automated technique for road network extraction from high-resolution multispectral satellite imagery of urban areas. Road segments are iteratively identified by examining contextual length-width features extracted from the multispectral imagery in conjunction with a vegetation index. A straight-line road segments are identified, the endpoints of these line segments are grown, allowing the road network extraction algorithm to track roads around curves and through area that are partially occluded. Long line segments are iteratively added to the road network, and a buffer is set up around them to exclude any line segments that are not close to perpendicular to the identified road network segments. This algorithm is fully automated and requires no interaction with the user after initial setting of several parameters controlling the identification pf potential road pixels, growth of the line segments, and the stopping criteria. The proposed approach yields an accurate road network with minimal interaction from the user. Extraction completeness measures of 82-85% and correctness measures of 71-84% are obtained.
Starting Page	461
Ending Page	463
File Size	1507205
Page Count	3
File Format	PDF
ISBN	0780379292
DOI	10.1109/IGARSS.2003.1293809
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Satellites Multispectral imaging Roads Image segmentation Iterative algorithms Image edge detection Feature extraction Image resolution Testing Urban areas
Content Type	Text
Resource Type	Article

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