Retrieving Vegetation Re-Radiation Patterns by means of Artificial Neural Networks
Caldeirinha, R. F. S.
; Cuinas, I.
IEEE Antennas and Wireless Propagation Letters Vol. 15, Nº 99, pp. 1097 - 1100, March, 2016.
ISSN (print): 1536-1225
Scimago Journal Ranking: 1,11 (in 2016)
Digital Object Identifier: 10.1109/LAWP.2015.2493515
Modelling vegetation usually implies obtaining experimental data by means of measurement campaigns. In general, the most accurate models need the re-radiation pattern of the vegetation volume under study, or some of its related parameters. Obtaining this function might not be easy, and the measurement procedure may introduce errors depending on the location of the vegetation mass. An accurate tool to simplify this process is presented, based on the ability of artificial neural networks to infer behavioral patterns. This proposal reduces the acquisition of the experimental data to a scarce set of angles around the tree or bush, which will then be used to train a neural network capable of retrieving the desired re-radiation function desired.