
Proceedings Paper
Evaluating automated road extraction in different operational modesFormat | Member Price | Non-Member Price |
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Paper Abstract
From an operational standpoint, road extraction remains largely a manual process despite the existence of several
commercially available automation tools. The problem of automated feature extraction (AFE) in general is a challenging
task as it involves the recognition, delineation, and attribution of image features. The efficacy of AFE algorithms in
operational settings is difficult to measure due to the inherent subjectivity involved. Ultimately, the most meaningful
measures of an automation method are its effect on productivity and actual utility. Several quantitative and qualitative
factors go into these measures including spatial accuracy and timed comparisons of extraction, different user training
levels, and human-computer interface issues.
In this paper we investigate methodologies for evaluating automated road extraction in different operational
modes. Interactive and batch extraction modes of automation are considered. The specific algorithms investigated are the
GeoEye Interactive Road Tracker®(IRT) and the GeoEye Automated Road Tracker®(ART) respectively. Both are
commercially available from GeoEye. Analysis metrics collected are derived from timed comparisons and spatial
delineation accuracy. Spatial delineation accuracy is measured by comparing algorithm output against a manually
derived image reference. The effect of object-level fusion of multiple imaging modalities is also considered.
The goal is to gain insight into measuring an automation algorithm's utility on feature extraction productivity.
Findings show sufficient evidence to demonstrate a potential gain in productivity when using an automation method
when the situation is warranted. Fusion of feature layers from multiple images also demonstrates a potential for
increased productivity compared to single or pair-wise combinations of feature layers.
Paper Details
Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341A (27 April 2009); doi: 10.1117/12.817740
Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341A (27 April 2009); doi: 10.1117/12.817740
Show Author Affiliations
Peter Doucette, National Geospatial-Intelligence Agency (United States)
Jacek Grodecki, GeoEye, Inc. (United States)
Richard Clelland, GeoEye, Inc. (United States)
Andrew Hsu, National Geospatial-Intelligence Agency (United States)
Josh Nolting, GeoEye, Inc. (United States)
Jacek Grodecki, GeoEye, Inc. (United States)
Richard Clelland, GeoEye, Inc. (United States)
Andrew Hsu, National Geospatial-Intelligence Agency (United States)
Josh Nolting, GeoEye, Inc. (United States)
Seth Malitz, GeoEye, Inc. (United States)
Christopher Kavanagh, National Geospatial-Intelligence Agency (United States)
Steve Barton, National Geospatial-Intelligence Agency (United States)
Matthew Tang, GeoEye, Inc. (United States)
Christopher Kavanagh, National Geospatial-Intelligence Agency (United States)
Steve Barton, National Geospatial-Intelligence Agency (United States)
Matthew Tang, GeoEye, Inc. (United States)
Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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