‚2012
on chain for
reshold. (3)
'd structures.
ising LiDAR
)EM data the
Looking into
in fact phase
of the scene,
trical decor-
scene, com-
segmentation
regularly lo-
ge buildings.
volume map,
mputation of
? operational
nfigurations.
nsidering the
egmentation
orders due to
ng the accu-
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are in Tab. 1.
ference over
ard deviation
ndard devia-
"TanDEM-X
ne, so intro-
| considering
it 0.6 meters.
VERATION
“the TanDEM-
urban DEM
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
a [ Mean Difference [m] | STD Difference [m] | Mean RMSE [m] | STD RMSE [m] |
LiDAR Segmentation 4.536 4.334 8.205 4.249
Common Segmentation 0.589 3.743 4.824 4.028
Table 1: Operational Urban TanDEM-X Raw DEM Accuracy
Volume Map TanDEM-X )
(LIDAR Segmentation
400
300
lat [pix]
200
100
0 100 200 300
lon [pix]
Figure 3: Volume map derived from TanDEM-X data using the
LiDAR segmentation result.
Difference Lidor-TanDEM-X (common). Mean: 0.589357[m] STO: 3.7432*
Try T TET TT =
20H
Marin TC
o EPIRI j Ay A
mmm m
clan dy
diff [m]
TTTT
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a
a
TTT
|
T
1
ilr " "n L i 1 CE 1
100 200 300 400
Building Number
OT
Figure 4: Buildings mean height difference between LiDAR and
TanDEM-X using for LiDAR segmentation. The red and blue
lines represent respectively the measured mean and the standard
deviation.
generation. The operational interferometric processing could be
modified towards optimal solutions for the processing of metropoli-
tan regions. The processing chain involves sequentially the spec-
tral filtering, the coregistration, the interferogram generation and
its multilooking, the phase unwrapping and the geocoding (Rossi
et al., 2012). Besides the range spectral filtering and the phase
unwrapping, which can be switched off depending on the scene
configuration, this paper analyzes modifications to the coregistra-
tion and multilooking stages. The modification to the geocoding
75
stage are here not yet studied.
3.1 Spectral Shift and Phase Unwrapping Stages
The spectral shift stage could be switched off in case of pure ur-
ban areas. The statistical base which justifies it, distributed scat-
tering, is generally not valid for municipal zones. Generally, the
geometrical configuration of the TanDEM-X mission is built to
obtain a small gain from the filtering, of about the 396-596. This
processing step can be thus enabled to obtain a small gain for
mixed scene configuration (rural, urban).
The phase unwrapping algorithm exploited in ITP is the Mini-
mum Cost Flow (MCF). If the overall height variations of the
scene are smaller than the height of ambiguity it could be in prin-
ciple switched off. Considering the mission planning it is anyhow
always turned on, as the first year height of ambiguity is around
45 meters and the second one around 35 meters. Scene height
variations not overcoming these boundaries are quite uncommon.
3.2 Coregistration Stage
The algorithm exploited in ITP is already optimized, with mis-
alignments well below the pixel (Yague Martinez et al., 2010).
Nevertheless, it can be configured through the coregistration win-
dow size and distance. In the HR spotlight case, the window size
is set to about 35 meters in azimuth direction and to 19 meters in
the range one. The distance between windows is respectively 70
and 30 meters. Tradeoffs between window size and desired accu-
racy were already predicted for the coherent case (Bamler, 2000).
Due to different statistics they are however not valid for urban
scenarios. In the urban case, large windows or large distances
may include different building with different heights, creating a
coregistration mismatch and a loss of coherence. For a standard
TanDEM-X scenario the loss of coherence can be quantified with
geometrical calculations. The result for different height discrep-
ancies in a coregistration window cell is in Fig. 5. Due to the
relatively small baselines of the helix formation of TanDEM-X
the loss is unimportant (a coherence of about 0.05 for a height
discrepancy of 100 meters). The ITP coregistration approach
can be thus considered already optimized. A small suggestion
would be the reduction of the window distance by a factor of 2.
It has been in fact empirically proven that the reduction of the
distance reduces the number of phase unwrapping residues by a
small amount.
3.3 Interferogram Generation Stage
The highly optimized moving average window employed to re-
duce the phase noise in the ITP multilook stage can be optimized
for urban modelling purposes. In particular, adaptive algorithms
making use of amplitude statistics to fuse pixels with the same
features are here analyzed. The algorithm in (Vasile et al., 2004),
connecting pixels with a region growing technique, and the one
in (Deledalle et al., 2011), connecting also not consecutive pixels
inside a search window, are tested. The need to employ adaptive
methods is clear looking at Fig. 7 and Fig. 8, portions of the
DEM for an high resolution spotlight acquisition over Las Vegas
acquired on the 25th September, 2011. The interferometric phase
in Fig. 6 is processed to obtain a mean theoretical resolution of
3.65 meters. The NL-InSAR algorithm (Deledalle et al., 2011)
is used for the multilooking and the coherence estimation. The
