Landcover classification using ERS SAR/INSAR data over tropical areas

Abstract

In this study, we 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 landuse maps and information derived from SPOT and Landsat data. We point out the usefulness of the coherence γtandem component for this application. Moreover, we show that Δγ (difference between γtandem and γ35-day) can give additional useful information for the classification process.