27 April 2009 Evaluating automated road extraction in different operational modes
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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.
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Peter Doucette, Peter Doucette, Jacek Grodecki, Jacek Grodecki, Richard Clelland, Richard Clelland, Andrew Hsu, Andrew Hsu, Josh Nolting, Josh Nolting, Seth Malitz, Seth Malitz, Christopher Kavanagh, Christopher Kavanagh, Steve Barton, Steve Barton, Matthew Tang, Matthew Tang, } "Evaluating automated road extraction in different operational modes", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341A (27 April 2009); doi: 10.1117/12.817740; https://doi.org/10.1117/12.817740
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