Generating future states in satellite imagery by neural networks

摘要

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. !2