Share Email Print
cover

Proceedings Paper

Evaluating automated road extraction in different operational modes
Author(s): Peter Doucette; Jacek Grodecki; Richard Clelland; Andrew Hsu; Josh Nolting; Seth Malitz; Christopher Kavanagh; Steve Barton; Matthew Tang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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
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)
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)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top