Separability analysis of multifrequency SAR polarimetric features for land cover classification

Polarimetric Synthetic Aperture Radar (PolSAR) imagery can provide valuable observables at different frequencies for classification tasks. In this paper, we assessed separability rate of various polarimetric features in three frequencies of X-, C-, and L-bands. To this end, Jeffries-Matusita distance was firstly used to measure separability of each polarimetric feature in each frequency band. Random Forest classifier was then applied to map various land cover classes in study area. The classification outputs indicated that C-band results were better and more reliable than L-band results and L-band results were subsequently better than X-band results. These results were perfectly compatible with the results obtained by the separability analysis of multifrequency PolSAR features.