Semi-supervised learning for classification of polarimetric SAR-data

Supervised learning algorithms are important methods to automatically interpret image data in general as well as PolSAR data in particular. However, they suffer from the need of a training set, which has to contain manually labelled data. Un-supervised methods do not demand this kind of data, but cannot be directly used to assign user-defined class labels to image regions. This paper proposes a semi-supervised method to overcome both shortcomings. The data is analysed by an un-supervised clustering algorithm under the usage of all available information. Simultaneously each pixel is classified by a supervised method using the information available at the current phase of clustering.