In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010,1 APRS, Vol. XXXVIII, Part 7B 
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Therefore, changes at the vegetation level could not be 
detected. In order to focus on the urban changes, a vegetation 
mask, computed from the IKONOS multispectral data, has 
been used. This mask has been generated using the 
Normalized Differenced Vegetation Index (NDVI). Figure 5 
displays the two DSMs used in this article after removing of 
the vegetation area applying the vegetation mask. 
3.2 Generation of the difference image. 
Following the procedure described in subsection 2.2, after the 
co-registration of the two DSMs depicted in Figure 5, we 
apply the “robust difference” approach to calculate the 
difference images, where noise has been reduced and changes 
can be better analyzed. As we can see from Figure 5, in the 
LiDAR-DSM, the building edges are sharper than in the 
IKONOS-DSM. Therefore, we choose a 7x7 window (w=3 in 
equations (1) and (2)) in the noise reduction procedure for 
this evaluation. 
Both the positive (which highlights the new constructed areas) 
and negative difference image (which highlights the 
destroyed areas) were generated in this step. Figure 6(a) 
displays the positive robust image difference obtained when 
applying a 7x7 window in the noise reduction step. The dark 
blue colour means no change, and the light blue colour 
corresponds to height changes of about 20 meters. 
3.3 3D change detection maps 
The different steps of the proposed 3D change detection 
method applied to the city centre of Munich are displayed in 
Figure 6. Figures 6 (a-c) present the positive change results. 
The positive difference image is displayed in Figure 6 (a). 
The corresponding change mask is shown in figure 6 (b). 
Figure 6 (c) depicts the final change detection map, under the 
assumption that each building has only one height value, 
computed as described in subsection 2.3. As can be seen from 
the change detection results, 6 new buildings are detected. 
The buildings No.4 and 5 are detected as one building in the 
mask map (showed in figure 6(b)), due to errors in the DSM 
generation procedure. To separate the two buildings, in the 
box-fitting procedure, we extract two seed points in this area 
based on image eroding result. And we use the original edges 
in the box growing procedure, so the image eroding will not 
influence the accuracy of the result. 
fitting these buildings to regular rectangular shape. This 
suggests them to be false alarms (as shown in Table 1). 
Therefore, only one building is remaining in the negative 
change map. 
The same steps have been adopted to detect the negative 
changes (presented in figures 6 (d-f)). According to the 
negative change detection results, four building masks are 
detected. But three of them are in strange shape, with relative 
low height values. Also, the box-fitting procedure fails in 
Table 1. Change Detection Result Comparison 
Figure 6. Change detection results: (a) Positive difference 
map; (b) Positive change mask; (c) positive change map; (d) 
Negative difference map; (e) Negative change mask; (f) 
Negative change map 
4. RESULTS AND DISCUSSION 
In order to allow a quantitative evaluation of the effectiveness 
of the presented methods, and also to study the influence of 
the building shapes on the change extraction procedure, we 
compare the mask-based change maps to the box-fitting 
based ones. A manually annotated change map has also been 
included in the evaluation scheme: 
No. 
Change Type 
Mask-Based 
Box-fitting Based 
Manual Extraction 
Height [m] 
Area [m 2 ] 
Height [ml 
Area 
KJ 
Height [m] 
Area [m 2 ] 
1 
Positive change 
19.58 
3247 
19.80 
3077 
22.0 
2788 
2 
Positive change 
19.10 
491 
16.46 
576 
17.00 
465 
3 
Positive change 
18.97 
3344 
19.10 
3748 
18.8 
3694 
4 
Positive change 
18.78 
2553 
19.55 
18.26 
1818 
2911 
19.2 
17.90 
1377 
2289 
5 
Positive change 
16.97 
1093 
16.6 
912 
6 
Positive change 
18.64 
1683 
18.69 
1409 
20.2 
945 
7 
Negative change 
-33.72 
762 
-32.42 
800 
-36.0 
1007 
8 
Negative change 
-6.2 
1044 
— 
- 
— 
— 
— 
— 
9 
Negative change 
-6.43 
578 
— 
- 
- 
— 
— 
— 
10 
Negative change 
-6.30 
565 
... 
- 
- 
- 
- 
-