SINCOHMAP LAND-COVER AND VEGETATION MAPPING USING MULTI-TEMPORAL SENTINEL-1 INTERFEROMETRIC COHERENCE
Abstract
InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Donana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.