## 3 Results
### Class Separability and Feature Selection  
Multivariate assessment was completed for all feature spaces of the decomposition elements of dual- and quad-polarized X-, C- and L-Band datasets. The separabilities between classes (for the feature spaces of interest) were quantified for all possible combinations of variables (41) using the Transformed Divergence (TD) (Table 4) and the squared Jefferys Matusita Distance (JD) (Table 5). The feature spaces were ranked in descending order based on the average separability (AV) by the feature space of interest. For the purpose of comparison the tables also list separabilities achieved with the multispectral Landsat TM data using the six spectral bands (thermal information was excluded). Results showed that the quad-polarized Kennaugh Matrices of ALOS-2 and R-2 offered the best separation of all land cover classes, followed by the HH/VV-polarized Kennaugh Matrices of ALOS-2, R-2 and TSX. The separability distances TD and JD further indicated that AV of ALOS-2 was comparable to the AV offered by multispectral data. As well, among the different PolSAR decompositions, the use of all Kennaugh Matrix elements was more beneficial for class separation than using the features of the model-based (power) Decompositions, Eigen-decompositions, or the intensities of the polarimetric channels. For C- and L-Band it was further observed that TD and JD of the Kennaugh Matrix decreased from quad-, to HH/VV-, to VV/VH- to HH/HV-polarized data. For X-Band it was observed that TD and JD of the Kennaugh Matrix decreased from HH/VV- to HH/HV-polarized data. The separability of the Eigen-decomposition features ENT, ALPT or ALPC and ANI was low, and these feature spaces, as indicated by JD, offered the lowest separability between classes among all investigated feature spaces; independent of the wavelength (X-, C- or L-Band). With HH/VV-polarized data, the high correlation of Entropy and the Alpha scattering angles was observed by others [50], though. Another reason for this might be the lack of diversity of scattering processes in this rather \"bare\" landscape, thus the Entropy/Alpha feature space remains \"unfilled\" to a certain degree. Specifically, the tundra landscape examined in this research, offers a minor depolarizing, and low entropy environment. The separability distance JD further outlined that the average class separability decreased from ENT/ALPT/ANI (quad) to ENT/ALPT (HH/VV) to ENT/ALPC (VV/VH) to ENT/ALPC (HH/HV). This might simply be a function of intensity, which decreased from quad to HH/VV to VV/VH to HH/HV, since with lower intensities there is also lower information content. Among the land cover classes the classes OL, BS, BW and WT were shown to be the land cover classes with the highest average separability, thus the PolSAR data were especially suited to characterize these classes. The lowest average separability was observed for the land cover classes BM and WI, while separability of HT and ST was moderately high. Table 6 draws the average increase of the separability features TD and JD for the ten elements of the quad-polarized Kennaugh Matrices (K0-K9) of C-Band (left column) and L-Band (right column) data. This assessment indicates that the average increase in separability when a feature of interest (K0-K9) is added to an existing feature space. This metric was used to identify the most important elements of the Kennaugh Matrix for class separation. For both C- and L-Band it was observed that K0, K1, K2, K3 and K4 were more important for class separation than K5, K6, K7, K8 and K9. An explanation for this observation is that K0, K1 and K2 are intensity-based elements and are thus value are generally stable. K3 and K4 also use the cross-polarized intensity and the co-polarized phase, and are therefore relatively stable as well. Contrarily, K5, K6, K7, K8 and K9 are phase-based elements and therefore generally unstable in natural environments.