In: Wagner W„ Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
building segments is enhanced using morphological opening of 
3 m and 4 m kernel size, respectively. For a training area 
containing 103 buildings two classification trees were derived 
independently. The features automatically selected for 
classification were area, shape index, and first-last-echo 
difference. The complexity of the derived trees is very low. The 
trees consist of one and two splitting nodes, respectively. 
Figure 6 shows a subsection of the classified building footprints 
from the autumn data set. 
Fig. 6. Classified building footprints from the autumn data set 
5.2 Change detection 
The comparison of the building footprints from the two epochs 
is done by a simple overlay of the segments. This leads to an 
extreme overestimation of changes since also small differences, 
which occurred from slightly different building outlines in both 
data sets (i.e. due to differing sensor position in each epoch, 
registration and rasterization) and uncertainty in the building 
detection method. 
The final result of the automatic building footprint change 
detection is shown in Figure 7. The overall accuracy of detected 
changes reaches 54%. This comes from a remaining 
overestimation of detected changes at demolished buildings. 
The plot in Figure 8 shows all the detected changes labelled by 
the actual changes derived from the reference. The changes are 
plotted by their area and height difference. It can be seen that 
the new and partly new buildings are detected very well 
reaching an overall accuracy of 90%. The problem arises for 
demolished and partly demolished buildings where the overall 
accuracy drops to 32%. This is mainly caused by trees and tree 
parts wrongly classified as buildings. Further changes not 
relating to buildings occur at the terrain such as road 
construction or come from extensive registration errors, which 
were apparent at the boundary of the test site. 
Theses wrongly detected changes are apparent as long and thin 
segment parts, which can be identified by calculating the shape 
index. Furthermore, the mean elevation within a segment part 
must not differ more than 3 m, which ensures that detected 
changes are equal or larger than the approximate floor height of 
a building. The overestimation of changes can be enhanced by 
selecting and relabeling segments parts based on their shape 
index, height difference between both DSMs, and area. 
Figure 9 shows the overall accuracy plotted as solid black line 
for areas from 0 to larger than 500 sqm with a bin size of 
50 sqm. The strong influence of changes caused by vegetation 
for segments smaller than 150 sqm is clearly indicated. If the 
vegetation removal could be improved, the overall accuracy for 
the 100 and 150 bin would increase to 100% and 75%, 
respectively as indicated by the dashed grey line.