Use of Poincare sphere parameters for fast supervised PolSAR land classification

We propose the use of Poincare sphere parameters for a fast supervised PolSAR land classification. The scattering matrix is represented by a point which indicates the polarization states on/in Poincare sphere. Then, by analyzing the distribution features of the points, the test area is classified into, for example, four types of targets: lake, grass, town and forest. This analyzing process can be implemented by employing a neural network. The experimental result shows that the Poincare sphere parameters are highly useful for classification. It is possible that the method will contribute to reduce the computational complexity of PolSAR classification process and provide higher accuracy.