Share Email Print

Proceedings Paper

Fusing multisensor and multisource data with implicit knowledge for monitoring
Author(s): Douglas L. Ramers
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 1 April 2003
PDF: 10 pages
Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.484919
Show Author Affiliations
Douglas L. Ramers, Univ. of North Carolina/Charlotte (United States)

Published in SPIE Proceedings Vol. 5099:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top