The purpose of this paper is to compare the performance of two feature extraction methods when applied on high resolution Synthetic Aperture Radar (SAR) images acquired with the new ESA mission SENTINEL-1 (S-1). The feature extraction methods were previously tested on high and very high resolution SAR data (imaged by TerraSAR-X) and had a good performance in discriminating between a relevant numbers of land cover classes (tens of classes). Based on the available spatial resolution (10x10m) of S-1 Interferometric Wide (IW) Ground Range Detected (GRD) images the number of detectable classes is much lower. Moreover, the overall heterogeneity of the images is much lower as compared to the high resolution data, the number of observable details is smaller, and this favors the choice of a smaller window size for the analysis: between 10 and 50 pixels in range and azimuth. The size of the analysis window ensures the consistency with the previous results reported in the literature in very high resolution data (as the size on the ground is comparable and thus the number of contributing objects in the window is similar). The performance of Gabor filters and the Weber Local Descriptor (WLD) was investigated in a twofold approach: first the descriptors were computed directly over the IW GRD images and secondly on the sub-sampled version of the same data (in order to determine the effect of the speckle correlation on the overall class detection probability).