An assessment of feature extraction methods for SENTINEL-1 images on urban areas

This paper makes a comparative assessment of the observable landcover classes visible in the data provided by the newly launched SENTINEL-1 (S-1) satellite. The analysis focuses on two feature extraction methods previously reported in the literature to be able to distinguish between a relatively large number of classes in high and very high resolution SAR data. The analysis of the S-1 medium resolution data makes use of the Gabor filtering and Fourier spectral coefficients. Moreover, we consider the opportunity of speckle reduction before feature extraction, considering that the texture analysis is sensitive to correlated speckle. Furthermore, the comparison takes into account the adaptation of the window size to the resolution and pixel spacing of the data. In order to make a quantitative assessment of the results, we perform a joint evaluation of the detection probability at pixel and patch level, with respect to an expert annotated dataset.