Classification of Multitemporal InSAR Data for Land Cover Mapping in Selenga River Basin, Mongolia

The aim of this study is to evaluate different features extracted from the multitemporal spaceborne interferometric synthetic aperture radar (InSAR) data sets for a rural land cover mapping. For the actual land cover classification, the traditional statistical maximum likelihood classification and neural network method are performed and the results are compared. Overall, the research indicated that the multitemporal InSAR data sets have a valuable contribution to efficient land cover mapping.