Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 8, Pages 1234-1238Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2831215
Keywords
Activation function; Poincare sphere parameter; polarimetric synthetic aperture radar (PolSAR); quaternion neural network (QNN)
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Funding
- JSPS KAKENHI [15H02756, 18H04105]
- KDDI Foundation
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Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphereparameter space. The high performance arises from the good generalization characteristics realized by a QNN as 3-D rotation as well as amplification/attenuation, which is in good consistency with the isotropy in the polarization-state representation it deals with. However, there are still two anisotropic factors so far which lead to a classification capability degraded from its ideal performance. In this letter, we propose an isotropic variation vector and an isotropic activation function to improve the classification ability. Experiments demonstrate the enhancement of the QNN ability.
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