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A polarimetric SAR data classification method using neural networks

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2 Author(s)
Ito, Y. ; Dept. of Civil Eng., Takamatsu Nat. Coll. of Technol., Japan ; Omatu, S.

Recently, neural network approaches have been adopted into polarimetric SAR data classification methods. A feature vector considering scattering effects, powers, and relative phases between polarimetries is presently being devised to discriminate more detailed categories. The authors propose a neural network classifier using polarization signatures and the above-mentioned feature vector. The polarization signature is composed of like- and cross-PSDs (Polarization Signature Diagram) which fully represent polarimetric features from scatters. The proposed method employs a maximum of σ0 in the like-PSD and a minimum of σ0 in the cross-PSD as input data for the neural network. The LVQ neural network is adopted as a classifier. Multi-frequency polarimetric SAR data observed by the quad-polarization mode of SIR-C were employed for the experiments. The proposed and conventional approaches are compared with average accuracies computed by classifying test data. As a result, the authors show that the proposed method is more useful and effective in producing classification accuracies

Published in:

Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International  (Volume:4 )

Date of Conference:

6-10 Jul 1998