1 April 2003 Fusing multisensor and multisource data with implicit knowledge for monitoring
Author Affiliations +
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
10 PAGES


SHARE
KEYWORDS
Earth observing sensors

Surface plasmons

Data fusion

Landsat

Temperature metrology

Data modeling

Satellite imaging

Back to Top