Land-cover Classification using Multi-temporal/polarization C-band SAR Data

This paper presents a fuzzy logic fusion methodology for land-cover classification with multi-temporal/polarization Radarsat-1 and ENVISAT ASAR data. For feature extraction from each multi-temporal/polarization data, a traditional feature extraction approach (i.e. extraction of average backscattering coefficient, temporal variability and long-term coherence) and principal component analysis (PCA) were applied and compared. A data-driven fuzzy logic approach was applied to the classification of those features. In the fuzzy logic approach, fuzzy membership functions based on smoothed kernel density estimation and likelihood ratio functions were derived and various fuzzy combination operators were tested. A case study from an agricultural area has been carried out to illustrate the proposed methodology.