A Comparative Evaluation of Polarimetric Distance Measures within the Random Forest Framework for the Classification of Polsar Images

Random Forests have been shown to able to be applied directly to polarimetric synthetic aperture radar (PolSAR) data instead of to extracted hand-crafted features by adapting the internal node tests. This paper investigates different polarimetric distance measures and their potential to be used by Random Forests for the classification of PolSAR images. The experiments show that using distance measures tailored towards the statistics of PolSAR data outperforms the usage of individual hand-crafted polarimetric features and their combination. However, the differences between accuracies obtained by different suitable distance measures are insignificant allowing to take other aspects into consideration such as computational efficiency.