Multi-temporal polarimetry in land-cover classification

ABSTRACTThis study uses time-series Sentinel-1(S-1) synthetic aperture radar images to evaluate the impact of multi-temporal polarimetric processing on land-cover classification. Various polarimetric processing methods are applied to multi-temporal S-1 data set in order to obtain several inputs parameters for land-cover classification: e.g. time-series coherence matrices from dual-polarization data (shows coherence among polarizations in matrix for separated time points t1, t2, to tn); scatter zone time series; multi-temporal single and dual-polarization coherence matrices (reveal coherences among time points for one or two polarizations); and parameters from the H/α decomposition. Then, the classification potential of each polarimetric data set is compared to a reference classification, which was derived from time series of dual-polarization backscatter (σ0) images. We evaluate if polarimetric processing of dual-polarization images brings better classification results than alone classification of backsca...