In: Wagner W„ Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
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stereo Cartosat-1 images using the DEM generation method of 
d’Angelo et al. (d’Angelo et al., 2009). We also use orthorecti- 
fied panchromatic Cartosat-1 image of the corresponding region. 
For better representation, we locate reconstructed buildings on a 
smoothed DTM of the region which is generated using method 
of Arefi and Hahn (Arefi and Hahn, 2005). Our panchromatic 
Cartosat-1 test images have 2.5 m. spatial resolution, however 
DEM and DTM have 5 m. spatial resolution. 
In the first row of Fig. 3, we represent the orthorectified panchro 
matic Cartosat-1 image, the original DEM, and the enhanced DEM 
for our Jeddai sample test image respectively. For a better visual 
representation we covered the DEM with the panchromatic image 
of the region. In the second row of Fig. 3, we represent another 
example from our test image dataset. In this row, we represent the 
orthorectified panchromatic Cartosat-1 image, the original DEM, 
and the enhanced DEM for our Jedda3 test image. As can be 
seen in these examples, the enhanced DEM data reflects building 
reconstruction in urban area more clearly. Besides, DEM errors 
which come from automatic the DEM generation method are also 
corrected in the enhanced DEM. However, we could not detect 
exact shapes of complex buildings and we can not discriminate 
some of the adjacent buildings, the final improvement in DEM 
data is informative. We will analyze detection of complex build 
ing shapes in our future studies. Next, we analyze performance 
of our proposed method on a sample test image to give a sight to 
possible readers. 
5.1 Performance Analysis on Sample Image 
We pick Jeddai test image to evaluate the performance of our 
method. To analyze performance we consider two measures; 
shape accuracy (pi) and height accuracy (p 2 ). First, we start 
with measuring shape accuracy of the shape approximation (Box- 
Fitting) approach. We use the method used by Ruether et al. 
(Ruether et al., 2002) to measure the shape accuracy. For a [to x 
n] size test image shape accuracy performance (pi) is calculated 
as follows, 
Pi = ( 
ZT=1 Hy=i 1 B f(z,y) - Bg th (x,y)I 
Y^=lYJ l y=l B 9th(x,y) 
) x 100 (7) 
in this equation Bf(x,y) is the binary image which is obtained 
by filling holes as ’1’ in B(x,y) binary image. B gt h is the bi 
nary groundtruth shape mask that we labeled buildings as ’ 1’ and 
other regions as ’0’ manually. We calculate pi value as 78,02% 
for Jeddai test image. Unfortunately, 53 of 66 buildings are de 
tected in the region. Therefore, our groundtruth masks includes 
some buildings which are not detected in building shape detec 
tion method, so those buildings are not labeled after our shape 
approximation method. Therefore, we obtain slightly low shape 
accuracy performance. If shape accuracy is calculated for each 
building one by one, we can observe higher shape accuracy per 
formance for each building. 
In order to calculate height accuracy, we first calculate each build 
ing height in Jeddai test image using panchromatic stereo CartoSat- 
1 images. Using triangulation techniques, we measure each build 
ing height manually and list obtained height values as vector data. 
We also list building heights in the same order measuring the 
heights in the final enhanced DEM data. We generated enhanced 
DEM both using mean and median values of building rooftop val 
ues. As a result, two enhanced DEM building height value vec 
tors are used in performance calculation. By subtracting groundtruth 
building height vector from these vectors, the differences can be 
obtained. In the ideal case, we expect to obtain zero values as dif 
ference. In order to measure height accuracy (p 2 ), we used RMS 
values of these difference vectors. For the vector generated by 
using the mean of DEM values, RMS of difference vector is cal 
culated as 1.80. For the vector generated by using the median of 
DEM values, RMS of difference vector is calculated as 2.63. We 
pick the method which generates p 2 value closer to zero. There 
fore, using mean value of DEM when calculating building heights 
gives more accurate results. 
5.2 Computation Times 
We finally analyze computation time needed for our method. The 
computation time of the proposed DEM enhancement method is 
also very impressive. For our sample Jeddai test image which is 
in [566 x 590] pixel sizes, we tabulate timing requirements of all 
modules in the DEM enhancement method in Table 1. We obtain 
these timings using an Intel Core2Quad 2.66GHz PC and Matlab 
coding environment. As can be seen in this table, segmenting ur 
ban area from DEM data requires only 0.28 seconds. We detect 
possible building locations in 1.74 seconds. The longest com 
putation time is needed for shape approximation (Box-Fitting) 
step. For Jeddai test image which includes 76 buildings, shape 
approximation step requires 65.14 seconds. In this step, timing 
directly depends on the test image. As the number of buildings 
increases in given test image, the shape approximation module 
needs more computation time. However, this module can run 
faster if it is coded in C. Finally, enhancing building shapes in 
DEM requires 0.82 seconds. Consequently, running our proposed 
DEM enhancement method on Jeddai test image requires 67.98 
seconds. This short computation time may lead for the proposed 
method to be used in fast damage and change detection applica 
tions. 
Unfortunately, our method is not able to detect exact shapes of 
very complex buildings. Therefore, edges of these buildings are 
not sharpened in DEM data. We will handle detection of complex 
building shapes in our future studies. 
Module 
Time (in Sec.) 
Urban area segmentation 
0.28 
Detecting buildings 
1.74 
Shape approximation (Box-Fitting) 
65.14 
Enhancing building shapes 
0.82 
TOTAL 
67.98 
Table 1: CPU Times (In Seconds) for DEM Enhancement on 
Jedda 1 test image 
6 CONCLUSIONS 
In this paper, we proposed a new method for automatic DEM 
enhancement based on building shape approximation. First, we 
detected the urban area using DEM. Then, we used panchromatic 
image of corresponding region to detect possible building centers. 
For this purpose, we extracted Canny edges of buildings in the 
previously detected urban area. After that, we applied distance 
transform to these edges to detect building centers. We used de 
tected edges and building centers to run the shape approximation 
algorithm. Extracted approximate shapes helped us to sharpen 
building edges, and to smooth rooftops in the DEM. We also cor 
rected errors in DEM, which appear due to stereo image matching 
errors in DEM generation. 
After extensive tests on very low resolution and noisy DEMs, we 
obtained encouraging results with our method. Comparing with