Polarimetric SAR classification using visual color features extracted over pseudo color images

Polarimetric SAR data have been used extensively for terrain classification applying primitives from various target decompositions as well as texture features. However, there is a source of information that has been neglected so far from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudocolor images. In this paper, we investigate and evaluate discrimination power of color features extracted over various pseudocolor images. Experiments are conducted over the San Francisco Bay region on RADARSAT-2 data by using Support Vector Machines. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared to the traditionally employed polarimetric SAR and texture features) within the application of land use and land cover classification.