Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land Classification in Poincare-Sphere Parameter Space
Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land Classification in Poincare-Sphere Parameter Space
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DOI：
10.1109/lgrs.2018.2831215
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发表时间：
2018-05
影响因子：
4.8
通讯作者：
Kazutaka Kinugawa;Fang Shang;Naoto Usami;A. Hirose
中科院分区：
文献类型：
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作者：
Kazutaka Kinugawa;Fang Shang;Naoto Usami;A. Hirose
四元数神经网络在庞加莱球参数空间中工作，可以对各种观测数据进行高精度的极化合成孔径雷达分类。高性能源于QNN实现的良好的泛化特性，如3-D旋转以及放大/衰减，这与它所处理的偏振态表示中的各向同性具有良好的一致性。然而，到目前为止，仍然有两个各向异性的因素，导致其分类能力从理想的性能下降。在这封信中，我们提出了一个各向同性的变化向量和各向同性的激活函数，以提高分类能力。实验表明，QNN的能力的增强。
Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphere-parameter 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.