Share Email Print

Proceedings Paper

Generating future states in satellite imagery by neural networks
Author(s): Eduardo M.B. Alonso; Valter Rodrigues; Maria Conceicao Amorim
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

In this paper the neural network concept is studied, as a non-linear dynamic system, for predicting spatiotemporal patterns. The relative behavior of two back-error propagation neural network (BPNN) configurations is investigated in the context of real world data from geostationary meteorological satellite (GOES) images. One of them explores only temporal information, the other one takes into account spatial-contextual pattern aspects. The results demonstrate that neural networks are a useful tool for time series prediction of spatial patterns. It means that with certain accuracy future states of a spatial phenomena can be generated before the satellite captures them in its next imaging.

Paper Details

Date Published: 16 December 1992
PDF: 6 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130867
Show Author Affiliations
Eduardo M.B. Alonso, Instituto de Pesquisas Espaciais (Brazil)
Valter Rodrigues, Instituto de Pesquisas Espaciais (United States)
Maria Conceicao Amorim, Instituto de Pesquisas Espaciais (Brazil)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top