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

We propose a quaternion neural-network-based land classification in Poincare-sphere-parameter space. By representing the Stokes vector on/in the Poincare sphere geometrically, we construct two analysis parameters, namely, the position vector and the variation vector, to describe the feature of a pixel in test area. Then, by employing a quaternion feedforward neural network, we generate successful classification results for detecting lake, grass, forest, and town areas. In comparison with the conventional C-matrix-based methods, the proposed method has higher classification performance, especially in detecting forest and town areas. Moreover, the classification result of the proposed method is not influenced by height information. This fact suggests that the proposed classification method can be used for complicated terrains.