tested to check whether they match with extracted features from 
images. To quantify the checking procedure, a similarity 
measurement is defined using robust geometric criteria. In the 
second step, a digital elevation model (DEM) is automatically 
generated from the DSM. From the difference of DSM and 
DEM an above-ground mask is derived. From this mask 
buildings are generated and compared with the existing 
database in order to detect new buildings. According to the 
author, the actual delineation of building outlines is not very 
accurate, mainly due to shadows. 
Malpica and Alonso (2010) developed an approach for urban 
change detection integrating multi-spectral satellite imagery, 
LiDAR point clouds and a GIS database. SVM (support vector 
machine) was used to classify the image, resulting in a 
probability layer for buildings. By intersecting the classification 
result with the GIS building layer the authors found an increase 
in the built up area of a few percent. 
Tian et al (2010) used stereoscopic satellite imagery, to detect 
height changes by computing the difference between the DSMs 
generated at different epochs. A rectangle was fitted to each 
extracted blob assumed to be a building. However, most blobs 
are highly curved, so the direction of the rectangle edges cannot 
be computed reliably. 
2. DESCRIPTION OF INPUT DATA AND PRE- 
PROCESSINHG 
Two pan-sharpened stereo pairs from IKONOS-2 (epoch 1) and 
GeoEye-1 (epoch 2), acquired on May-24, 2008 and Sept.-15, 
2009 with ground sampling distances (GSD) of 1m and 50cm 
respectively, and depicting a suburb of Riyadh the capital of 
Saudi Arabia, are used in our study. The slant angle is 11? 
toward West for both and the base-to-height ratio is similar with 
1:1.75 for IKONOS and 1:1.51 for GeoEye. Figure 1 shows 
parts of the images used for our study. 
In addition we have at our disposal a GIS database showing all 
the buildings existing in the area in May 2008. Reference data 
for the building change detection study were generated 
  
Figure 1. Sample of building changes (a) and (c): GeoEye 2009, 
(b) and (d): IKONOS 2008 
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 
   
manually by delineating and comparing the buildings visible at 
the two epochs. 
Image orientation was provided by means of Rational 
Polynomial Coefficients. For the subsequent DSM generation 
we use semi-global matching (SGM, Hirschmüller, 2008). We 
thus need to transform the images into epipolar geometry (or at 
least something close to it, since for line images epipolar 
geometry in the strict sense does not exist). In our case it was 
sufficient to rotate the images around the viewing direction 
resulting in the x-axes of both image coordinate systems being 
parallel to the base, followed by a shift of 2.5 pixels in y- 
direction. 
As mentioned image matching was carried out using SGM 
(Hirschmüller, 2008) resulting in DSMs for both epochs. The 
grid spacing was set to Im for both DSM to have comparable 
conditions. 
3. BUILDING CHANGE DETECTION USING DSM 
SUBTRACTION 
This section deals with the detection of building changes by 
comparing the DSMs of the two epochs. We quickly found that 
simply taking the difference between both datasets did not yield 
useful results, mainly since georeferencing on the basis of 
rational polynomial coefficients (RPC) was not accurate enough 
(see Figure 2, a threshold of 2.5m for the absolute difference 
was used to show height changes). We thus applied a shift in all 
three coordinates to the second DSM with respect to the first. It 
was computed automatically using 3D least squares image 
matching similar to (Heipke et al., 2002) and amounted to 7.2m 
in X, 1.7m in Y and 1.3m in Z. 
TUE 
e 
= ; E 
€ gam TB = 
AR es noto T x aps * 
mo v v M SUME 
s > 2 f 
JE iA 
Figure 2. (a) IKONOS DSM, (b) GeoEye DSM, (c) and (d) 
binary change maps with D-DSMs larger than 2.5m: (c) before 
(red), and (d) after (blue) shift elimination 
After shift elimination, the differences in height were computed 
for each position in object space, see equation 1. 
AH (7) 5 H HC) A "UG. (D