Genetic algorithm identification of alternative sensor parameter sets for monitoring

Douglas L. Ramers

doi:10.1117/12.485710

25 August 2003 Genetic algorithm identification of alternative sensor parameter sets for monitoring

Douglas L. Ramers

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.485710
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Douglas L. Ramers "Genetic algorithm identification of alternative sensor parameter sets for monitoring", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.485710

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