Landcover classification using ERS SAR/INSAR data over tropical areas

The authors present a classification method using both optical and SAR data in order to perform landcover classification. First, different relevant parameters are derived from ERS-SAR data using multitemporal and interferometric analysis. Optical data are then used to define a set of training areas in order to perform a supervised classification. The results of classification using these techniques are compared with existing land use maps and information derived from SPOT and Landsat data. The authors point out the usefulness of the coherence γ_tandem component for this application. Moreover, they show that Δγ (difference between γ_tandem and γ_35-day) can give additional useful information for the classification process