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Proceedings Paper

Automated feature discrimination for optimizing water supply networks
Author(s): Richard D. Hedger; Tim J. Malthus; Frances Taylor
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Paper Abstract

The traditional approach to delineating and extracting features from remotely sensed images relies predominantly on manual interpretation, a procedure that is often time consuming and expensive. Automation offers the potential for reduced costs and wider utilization of remote sensing within the business community, but involves difficulty in representing the expertise of remote sensing scientists within a series of decision rules. The objectives of this paper are two-fold: firstly, to produce a system for automated feature discrimination in remotely sensed images, using leaks from water supply networks as a case study; and secondly, to test whether the system is suitable for use with the next generation of satellite sensors. The automated system was calibrated by integrating HyMap and Airborne Thematic Mapper (ATM) images with context data from a variety of sources (such as ambient irradiance environment; topography; land use, and field boundaries). The automated system was assessed for its applicability to satellite remote sensing by testing the system on airborne data that were degraded to the resolutions of satellite images. It is proposed in this paper that automation, particularly with respect to satellite remote sensing, makes leak detection from water supply networks commercially viable.

Paper Details

Date Published: 14 March 2003
PDF: 10 pages
Proc. SPIE 4886, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, (14 March 2003); doi: 10.1117/12.462355
Show Author Affiliations
Richard D. Hedger, Univ. of Edinburgh (United States)
Tim J. Malthus, Univ. of Edinburgh (United States)
Frances Taylor, Univ. of Edinburgh (United States)

Published in SPIE Proceedings Vol. 4886:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II
Manfred Ehlers, Editor(s)

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