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This Talk presents and discusses advanced neural networks by focusing on complex-valued
neural networks (CVNNs) and their applications in the remote sensing and imaging fields. CVNNs
are suitable for adaptive processing of complex-amplitude information. Since active remote sensing
deals with coherent electromagnetic wave, we can expect CVNNs to work more effectively than
conventional neural networks or other adaptive methods in real-number space. Quaternion (or
Hypercomplex-valued) neural networks are also discussed in relation to polarization information
processing. The beginning half of the Talk is devoted to presentation of the basic idea, overall
framework, and fundamental treatment in the CVNNs. We discuss the processing dynamics of
Hebbian rule, back-propagation learning, and self-organizing map in the complex domain. The latter
half shows some examples of CVNN processing in the geoscience and remote sensing society (GRSS)
fields. Namely, we present distortion reduction in phase unwrapping to generate digital elevation
model (DEM) from the data obtained by interferometric synthetic aperture radar (InSAR). In
polarization SAR (PolSAR), we apply quaternion networks for adaptive classification. Another
example is ground penetrating radar (GPR) to visualize underground objects to distinguish specific
targets in high-clutter situation. Finally we discuss the prospect of the CVNNs in the GRSS fields.
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论文共 11 篇作者统计合作学者相似作者
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#Papers: 11
#Citation: 210
H-Index: 9
G-Index: 10
Sociability: 3
Diversity: 2
Activity: 0
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