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Andrew Hsu

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Proceedings Article | 27 April 2009 Paper

Evaluating automated road extraction in different operational modes

Peter Doucette, Jacek Grodecki, Richard Clelland, Andrew Hsu, Josh Nolting, Seth Malitz, Christopher Kavanagh, Steve Barton, Matthew Tang

Proceedings Volume 7334, 73341A (2009) https://doi.org/10.1117/12.817740

KEYWORDS: Roads, Feature extraction, Image fusion, Detection and tracking algorithms, RGB color model, Tolerancing, Multispectral imaging, Algorithm development, Earth observing sensors, High resolution satellite images

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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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