Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land Classification in Poincare-Sphere Parameter Space

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

Averaged Stokes Vector Based Polarimetric SAR Data Interpretation

In this paper, we propose a new polarimetric synthetic aperture radar (SAR) data interpretation method based on a locally averaged Stokes vector. We first propose a method to extract discriminators from all three components of the averaged Stokes vector. Based on the extracted discriminators, we build four physical interpretation layers with ascending priorities, i.e., the basic structure layer, the low-coherence targets layer, the man-made targets layer, and the low-backscattering targets layer. An intuitive final image can be generated by simply stacking the four layers in the priority order. We test the performance of the proposed method over Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS-PALSAR) data. Experimental results show that the proposed method has high interpretation performance, particularly for skew-aligned or randomly distributed buildings and isolated man-made targets such as bridges

A case of matrix-producing carcinoma of the breast

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