International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
TanDEM—X DEM 
  
ma 
  
100 & 
  
200 
lon [pix] 
Figure 1: TanDEM-X operational raw DEM over the western part 
of Munich. 
sequential operations of clumping and sieving are used respec- 
tively for connecting the gaps and removing isolated regions of 
the thresholded DBM. À segmentation operation is then needed 
to label the obtained buildings. A sub-product of this approach is 
a volume map: 
VN = VN Aton Mat S hn, 
where Viv is the volume computed for the N detected building, 
ny the number of pixels composing the building, h, the local 
heights and A the DEM pixel spacing. The chosen site is Mu- 
nich, with the operational Raw DEM acquired on the 31st Oc- 
tober, 2010 used as test. The exploited Digital Terrain Model is 
the Digitales Gelaendemodell 25 (DGM25) from the Bayerische 
Vermessungsverwaltung. As the two models differ in resolution 
(DGM25 has 25 meters sampling), a resampling of the DGM25 
on the TanDEM-X Raw DEM is firstly performed. The available 
LiDAR DEM covering the same area has a fine resolution of 0.5 
meters, and is also resampled to the TanDEM one. In Fig. 1, the 
portion of the Raw DEM used for the validation is represented. 
The results of the processing chain employed for the building de- 
tection in the TanDEM-X Raw DEM are shown in Fig. 2. The 
same chain is exploited for the LIDAR DEM. The difference be- 
tween TanDEM-X and DGM25 is shown at the top left (mark 
(1)). The binary thresholding results are at the top right (mark 
(2)). The threshold is set to 5 meters. The LiDAR result is already 
satisfactory and a morphological operation could be avoided. On 
the contrary, the TanDEM-X result is noisier and a morphological 
operation to ensure the extraction of regularly shaped building is 
recommended. The results after the morphological stage are at 
the bottom left (mark (3)). The TanDEM result, even if not ho- 
mogeneous like the LiDAR one, allows the detection of isolated 
buildings. The segmentation result, at the bottom right (mark 
74 
  
Figure 2: Processing result of the volume derivation chain for 
TanDEM-X Raw DEM. (1) DBM. (2) Binary Threshold. (3) 
Morphological operations. (4) Segmentation. 
(4), color table at the bottom), numerates the detected structures. 
The TanDEM-X data overestimates the structures: using LiDAR 
data 403 structures are detected, whereas using TanDEM data the 
detection count is 437 (an increase of about 8%). Looking into 
detail the errors, the wrongly detected buildings are in fact phase 
unwrapping errors (as the region at the upper left of the scene, 
which is a flourishing park) or a mixture of geometrical decor- 
relations (as the district at the bottom right of the scene, com- 
posed by small houses). A good accordance in the segmentation 
is achieved at the upper right district, composed by regularly lo- 
cated buildings. There is a general accordance for large buildings. 
The LiDAR segmentation, represented through the volume map, 
is shown in Fig. 3. 
The two segmentation results are exploited for the computation of 
the buildings height difference. The accuracy of the operational 
stripmap TanDEM-X urban DEM is tested in two configurations. 
The accuracy of the mean height of the buildings considering the 
LiDAR segmentation is firstly tested. The LiDAR segmentation 
may differ with the TanDEM one at the building borders due to 
layover and shadow (Fig. 2 (4) and Fig.3) decreasing the accu- 
racy. Secondarily, the common building areas, considering the 
LiDAR segmentation, is tested (Fig. 4). The results are in Tab. 1. 
Considering the LIDAR segmentation, the mean difference over 
all the detected buildings is 4.5 meters with a standard deviation 
in the same order size. The relatively high mean/standard devia- 
tion is caused by a dozen of buildings for which the TanDEM-X 
building geolocalization is not fitting the LiDAR one, so intro- 
ducing also ground components in the measure. Just considering 
the common covered area the mean decreases to about 0.6 meters. 
3 EXPERIMENTAL URBAN RAW DEM GENERATION 
The high resolution allowed by the X-band sensors of the TanDEM- 
X mission dramatically increases the potentials of a urban DEM