XIX-B3, 2012
uilding areas which
ftop is classified as
s depending on the
tected as a complex
r is equal to 1), then
op in 3D space. To
segment using Har-
tephens, 1988) over
ides, we also detect
x each building cor-
ridge-line endpoint.
sest ridge-line end-
A detailed demon-
macek et al., 2012).
ed to corresponding
detected as a com-
uld not be extracted
ne idea for building
oftop reconstruction
building rooftop re-
ing rooftops and for
oe-line properly, we
D ; (x, y) matrix. Fi-
(x, y) binary matrix
red DSM values) to
SMs which are ob-
parts of the city of
ined from the satel-
2 and will show the
id enhance building
All satellite data are
ym different dates is
nd different shadow
density and quality
rd the Indian satel-
wo pushbroom cam-
D) of approximately
delivered with RPC
; equivalent to Level
itrol using an affine
uired in May 2008.
0 km, equivalent to
a GSD of 1 m and
is quite suitable for
1., 2010). The data
) km and have been
is “radiometric cor-
0.5 m and are com-
lay and orbit with an
t viewing directions
hese data have been
evel "Level 2 Ortho
on a plain for each
zes is about 2 km X
r data, generated by
dure (d'Angelo and
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Reinartz, 2011), have the same sampling distance as the image
data and cover all parts of the city center of Munich.
Two of the DSMs are obtained from airborne cameras, namely
the High Resolution Stereo Camera (HRSC) and the 3K-camera
system, both operated by DLR. The HRSC-AX (High Resolu-
tion Stereo Camera Airborne Extended) is a pushbroom camera
and is especially designed to acquire stereo aerial photographs
from 5 viewing directions. The image data exhibit a GSD of 1 m
while the DSM, generated as described in (Hirschmueller et al.,
Springer, DAGM 2005, Wien, Austria).
The DLR 3K-camera system consists of three off the shelf digital
frame cameras (Canon EOS 1Ds Mark, 16 MPixel) mounted on
a platform, of which one is pointed nadir, the second pointed to
the left and the third is pointed to the right according to the flight
direction (Kurz et al., 2008). The data used are acquired from
a flight altitude of approximately 1500 m, leading to a GSD of
25 cm The DSM has a GSD of 50 cm and is derived using images
with 8096 overlap leading to 4 viewing directions per object. The
airborne stereo data also cover parts of the Munich city center.
Therefore for further investigations an area is used where DSM
data from all sensors are available.
The sixth DSM, which is mainly used as a reference was ac-
quired by an Airborne Laser Scanning (ALS) system and pro-
vided through the Bavarian Surveying Authorities, the 3D point
cloud with a density of 2 points per square meter has been inter-
polated to a regular sampling distance grid of 1 m spacing.
42 Evaluation of Performances
In this section, we discuss the automatic 3D modelling perfor-
mance of the proposed system on six different DSMs of a test
building. In order to be able to calculate quantitative values for
shape detection performance analysis, we use a binary building
shape mask which includes groundfloor shapes of the buildings.
This mask has been prepared by Munich municipality by using
cadastral data. We apply pixel based performance analysis by
counting the number of correctly detected building groundfloor
pixels (True Positives - TP), and the number of false detected
building groundfloor pixels (False Alarm - FA). By dividing the
obtained numbers to the total number of building groundfloor pix-
els appearing in the groundtruth mask, in Table 1 we present TP
and FA numbers as percentages. As can be seen in this table, the
highest detection performance is obtained for the 3K, HRSC and
LIDAR sensor DSMs, as expected since these are all derived from
airborne data. But it has to be noted that also the WV2 DSM pro-
vides quite good results, with a quite low FA rate. On the other
hand, the lowest detection performance is obtained on Cartosat1
sensor DSM since the low resolution of this sensor does not al-
low seeing neighbored building segments separately. The highest
false alarm rate is obtained again on the Cartosatl sensor DSM
for the same reason. The lowest false alarm rate is obtained with
LIDAR sensor DSM because of its high spatial resolution and
since this DSM does not include trees which generally appear
connected to the building facades.
In order to provide an insi ght on height estimation performances,
we visualize a demonstration in Fig. 2. In Fig. 2.(a), we repre-
sent a profile of the sample building where we take height values.
In 2.(b), the red dashed profile represents original WV2 DSM
values, black dashed profile represents the LIDAR DSM values,
while the blue continuous line represents automatically obtained
Teconstruction values by using WV2 DSM.
Finally, we compare the automatic reconstruction method that we
Propose herein with our previous reconstruction method which
is based on active shape growing using building edge informa-
tion (Sirmacek et al., 2012). On the same building sample of
WV2 DSM, our previous approach gives performance values as
TP = % 65.02 and FA = % 34.97. Unfortunately, the previous ap-
proach cannot obtain successful detection results when the build-
ing edges cannot be detected correctly because of the noisy DSM
or because of the objects on the building rooftops.
DSM Sensor || TP (%) | FA (%)
Cartosatl 14.34 98.56
Ikonos 78.96 22.00
World View-2 89.83 10.16
3K 94.01 6.62
HRSC 95.09 14.81
LIDAR 95.02 0.81
Table 1: Pixel Based Building Groundfloor Detection Perfor-
mances for Six Different DSMs
ims
ses
(a) (b)
Figure 2: (a) The slice of the sample building where the hight
values are taken. (b) Red dashed profile represents original WV2
DSM values, black dashed profile represents the LIDAR DSM
values, blue continues line represents automatically obtained re-
construction values by using WV2 DSM.
5 CONCLUSIONS
Developing remote sensing technology and methods offer new
and low-cost approaches such as DSM generation based on stereo
satellite image matching principle. Herein, we introduced a novel
method for automatic 3D detailed city modeling based on build-
ing shape, tower, and rooftop ridge-line extraction. Using the pro-
posed approach we could generate 3D city models with high de-
tails even by using satellite images. Especially for regions which
cannot be covered by airborne measurements, or for fast map up-
dating or damage assessment purposes these data are well suited.
Besides proposing a novel and robust approach for 3D city mod-
eling, we provided a detailed assessment of the algorithm perfor-
mance for different sensor data. For this purpose, we used DSMs
which are obtained from different satellite (Cartosat-1, Ikonos,
WorldView-2) and airborne sensors (3K camera, HRSC, and LI-
DAR). Beyond the development of the fine detailed 3D city mod-
els, we believe that the provided performance analysis over differ-
ent sensor DSMs presents an important information about the ca-
pabilities of the different sensors and their remotely sensed stereo
data.
REFERENCES
Arefi, H., Engels, J., Hahn, M. and Mayer, H., 2008. Levels of de-
tail in 3d building reconstruction from lidar data. In Proceedings
of International Archives of Photogrammetry, Remote Sensing,
and Spatial Information Sciences 37, pp. 485-490.
Benedek, C., Descombes, X. and Zerubia, J., 2009. Building ex-
traction and change detection in multitemporal aerial and satel-
lite images in a joint stochastic approach. INRIA, Paris, France,
Tech. Rep.
