1 April 2003 Fusing multisensor and multisource data with implicit knowledge for monitoring
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This paper describes a series of experiments in data fusion of remotely sensed multispectral satellite imagery, in-situ physical measurement data (temperature, pH, salinity), and implicitly encoded knowledge (contained in location and season) to predict values and classified levels of chlorophyll-a using an artificial neural net (ANN). ANNs inherently fuse data inputs and discover relationships to provide a fused interpretation of the inputs. The experiments investigated the effects of fusing data and knowledge from the three different types of sources: non-contact, physical contact, and implicit. The results indicate that fusing the three source types improved prediction of chlorophyll-a values and classification levels, and that the multisource ANN fusion approach might improve or augment present periodic sample point monitoring methods for chlorophyll-a.
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Douglas L. Ramers, "Fusing multisensor and multisource data with implicit knowledge for monitoring", Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.484919; https://doi.org/10.1117/12.484919

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