The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
them perform well in terms of completeness. The differences 
almost entirely appear in terms of correctness: classification- 
based methods, i.e. (Matikainen et al., 2007) and (Rottensteiner, 
2007), seem to be more efficient than (Champion, 2007) and 
deliver fewer FPs. The per-building correctness rates obtained 
with the three approaches (0.54, 0.58 and 0.45, respectively) are 
relatively low, and none of the approaches appears to be a 
viable basis for a practical solution. However, this consideration 
must be modulated by the good correctness values (of 0.79, 
0.83 and 0.75), that are computed on a per-pixel basis. Since the 
per-pixel values are directly linked to the area that has been 
classified correctly or not, these values clearly highlight that the 
change detection is mostly uncertain for small buildings. 
(a) Matikainen 
(b) Rottensteiner 
(c) Champion 
Figure 1. Change Detection Evaluation in Marseille Test Area. 
In green, TP cases; in red, FN cases; in orange, FP 
cases; in blue, 77V cases. 
4.2 Toulouse Test Area 
4.2.1 (Rottensteiner, 2007) 
The evaluation of this method is illustrated in Fig. 3-a. Again, 
major changes are well detected. However, changes affecting 
small buildings are missed, which results in a high number of 
FNs for small buildings (Fig. 4-a). There are also many FPs 
that are mostly caused by inaccuracies in the DSM. Shadow 
areas are also systematically overestimated in the DSM, which 
generates FPs during the detection of new buildings. Two very 
large areas of false alarms appear in the eastern part of the 
scene (a sports field - Fig. 4-b) and in the north-eastern comer 
(a parking lot) and are related to classical problems of stereo 
matching algorithms, namely repeating patterns (demarcation 
lines in the sports field, rows of cars on the parking lot) and 
poor contrast. This entails height variations larger than 4 m in 
the surface model in areas that are essentially horizontal. 
4.2.2 (Champion, 2007): The evaluation of this method is 
illustrated in Fig. 3-b. Overall, the results are similar to those 
computed by (Rottensteiner, 2007) and particularly poor for 
small buildings (Fig. 4-c). The difficulty to extract pertinent 
primitives for small buildings entails 8 FN cases during the 
detection of new buildings and many FPs during the 
verification of the database (cf. Fig. 4-d for an example). 
(a) Matikainen: FP case (b) Matikainen: FP case 
(c) Rottensteiner: FN case (d) Rottensteiner: no FP case 
(e) Champion: FN cases (f) Champion: FP cases 
Figure 2. Evaluation details in Marseille. The same colour 
code as Fig. 1 is used. 
4.2.3 Remark concerning the satellite context