Generating future states in satellite imagery by neural networks

Eduardo M.B. Alonso; Valter Rodrigues; Maria Conceicao Amorim

doi:10.1117/12.130867

16 December 1992 Generating future states in satellite imagery by neural networks

Eduardo M.B. Alonso, Valter Rodrigues, Maria Conceicao Amorim

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130867
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Eduardo M.B. Alonso, Valter Rodrigues, and Maria Conceicao Amorim "Generating future states in satellite imagery by neural networks", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130867

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