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.
%0 Journal Article
%1 dlr130016
%A Geiß, Christian
%A Pelizari, Patrick Aravena
%A Bauer, Stefan
%A Schmitt, Andreas
%A Taubenböck, Hannes
%D 2019
%I IEEE - Institute of Electrical and Electronics Engineers
%J IEEE Geoscience and Remote Sensing Letters
%K DLR Taubenboeck LSFE
%N 3
%P 456--460
%T Automatic Training Set Compilation with Multisource Geodata for DTM Generation from the TanDEM-X DSM
%U https://elib.dlr.de/130016/
%V 17
%X 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.
@article{dlr130016,
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.},
added-at = {2020-10-29T10:14:13.000+0100},
author = {Gei{\ss}, Christian and Pelizari, Patrick Aravena and Bauer, Stefan and Schmitt, Andreas and Taubenb{\"o}ck, Hannes},
biburl = {https://www.bibsonomy.org/bibtex/225025f8b63bc2d74cde9e0541d575aba/earthobs_uniwue},
interhash = {c60de209db519e453123cfeb8d44e10a},
intrahash = {25025f8b63bc2d74cde9e0541d575aba},
journal = {IEEE Geoscience and Remote Sensing Letters},
keywords = {DLR Taubenboeck LSFE},
month = {Juli},
number = 3,
pages = {456--460},
publisher = {IEEE - Institute of Electrical and Electronics Engineers},
timestamp = {2020-11-18T22:08:33.000+0100},
title = {Automatic Training Set Compilation with Multisource Geodata for DTM Generation from the TanDEM-X DSM},
url = {https://elib.dlr.de/130016/},
volume = 17,
year = 2019
}