Polarization Feature Extraction Using Quaternion Neural Networks for Flexible Unsupervised Polsar Land Classification

We propose an unsupervised PolSAR land classification system consisting of quaternion auto-encoder and quaternion self-organizing map (SOM). Most of the conventional methods extract features necessary for the land classification based on a few of scattering models predefined by human beings. However, we can not expect classification into a large number of land categories recognizable to humans by using such restricted features. In this paper, we propose a method employing quaternion auto-encoder and quaternion SOM for feature extraction and classification, respectively. As a result, we succeed in discovering new and more detailed land categories. For example, town areas are divided into residential areas and factory sites.