Automatic Training Set Compilation with Multisource Geodata for DTM Generation from the TanDEM-X DSM

Abstract

The TanDEM-X mission (TDM) is a spaceborne radar interferometer which delivers a global digital surface model (DSM) with a spatial resolution of 0.4 arcsec. In this letter, we propose an automatic workflow for digital terrain model (DTM) generation from TDM DSM data through additional consideration of Sentinel-2 imagery and open-source geospatial vector data. The method includes the automatic and robust compilation of training samples by imposing dedicated criteria on the multisource geodata for subsequent learning of a classification model. The model is capable of supporting the accurate distinction of elevated objects (OBJ) and bare earth (BE) measurements in the TDM DSM. Finally, a DTM is interpolated from identified BE measurements. Experimental results obtained from a test site which covers a complex and heterogeneous built environment of Santiago de Chile, Chile, underline the usefulness of the proposed workflow, since it allows for substantially increased accuracies compared to a morphological filter-based method.