期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 2, 期 1, 页码 3-7出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2004.839648
关键词
buried objects; ground-penetrating radar (GPR); microwave imaging; neural network (NN)
In this letter, neural networks (NNs) are used to reconstruct the geometric and dielectric characteristics of buried cylinders. The NN is designed to work with input data extracted from the transient electric fields scattered by the target. To this aim, a simple simulation of a typical ground-penetrating radar setting is performed and different sets of data examined. Moreover, different neural network algorithms have been exploited, and results have been compared. Finally, the robustness of the proposed approach has been tested against noisy data and against uncertainties in the modelization.
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