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Proceedings Paper

Genetic algorithm identification of alternative sensor parameter sets for monitoring
Author(s): Douglas L. Ramers
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Paper Abstract

We are faced with the problem of identifying and selecting the most significant data sources in developing monitoring applications for which data from a variety of sensors are available. We may also be concerned with identifying suitable alternative data sources when a preferred sensor may be temporarily unavailable or unreliable. This work describes how genetic algorithms (GA) were used to select useful sets of parameters from sensors and implicit knowledge to construct artificial neural networks to detect levels of chlorophyll-a in the Neuse River. The available parameters included six multispectral bands of Landsat imagery, chemical data (temperature, pH, salinity), and knowledge implicit in location and season. Experiments were conducted to determine which parameters the genetic algorithms would select based on the availability of other parameters, e.g., which parameter would be chosen when temperature wasn't available as compared to when near infrared data was not available.

Paper Details

Date Published: 25 August 2003
PDF: 11 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.485710
Show Author Affiliations
Douglas L. Ramers, Univ. of North Carolina at Charlotte (United States)


Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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