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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
In order to test the accuracy of the classifications in a change- 
detection process, the classified buildings were compared with 
the building objects in the Ordnance Survey topographic 
database (used to generate the OS MasterMap® product). In 
the test area, all the significant building objects which were 
either greater than 50 m 2 in area, or had a postal address (and 
hence were residential buildings) were determined from the 
topographic database. In total, this revealed 965 buildings to 
test, of which 34 had been demolished. In addition to these, 
there were 17 significant new buildings on the image, making a 
total of 51 Category A changes. 
4.2 Detecting demolitions and new buildings 
Both the decision tree and object based classifications were 
tested using the same method of post-classification change 
detection. Demolitions and new buildings were considered 
independently. 
Demolitions were identified by intersecting the areas classified 
as buildings with the known OS MasterMap® building 
polygons. For each building polygon, if at least 50% of its area 
was classified as a building, that building polygon was 
considered to be verified by the classification. If less that 50% 
of its area was classified as a building, then it was considered to 
be a change (i.e. the building was considered to have been 
demolished). 
To identify new buildings, the first step was to mask out all the 
regions in the test site where constructions would be unlikely to 
have occurred. These consisted of all roads, rail or water 
bodies present in the OS MasterMap® data. All existing 
buildings in the data were also masked out, together with a 3 m 
buffer around each building, to help eliminate any remnant- 
objects produced by misalignment between the image and the 
topographic data or by the draping effect of the DSM. The 
remaining area, consisting of vegetation, farmland and man 
made surfaces, was then searched for any objects classified as 
buildings. Objects smaller than a given size threshold were 
filtered out, to leave a set of potential new buildings. 
4.3 Results of post classification change detection 
Table 2 shows the results for the decision tree classification, 
and Table 3 shows the results for the object-based classification. 
It can be seen in both cases that, of the 51 Category A changes 
on the image, 49 were successfully identified, with only one 
actual change not flagged as a change (false negative) each for 
demolitions and for new buildings. These errors were caused 
by a single feature - a residential building that had been 
demolished and rebuilt with a similar footprint. Such rebuilds 
are inevitably difficult to detect when the footprint in the map 
database of the recently demolished building is similar to the 
footprint in the image of the building constructed in its place. 
Demolitions 
New 
Total 
Actual changes 
34 
17 
51 
Objects classified as 
changes 
288 
161 
449 
Actual changes correctly 
classified (True Positives) 
33 
16** ' 
49 
Non-changed objects 
classified as change 
(False Positives) 
255 
150 
405 
Actual changes not 
classified as changes 
(False Negatives) 
1 
1 
2 
% Classified as changes 
that were actual changes 
11% 
10%** 
11% 
% of actual changes 
classified as changes 
97% 
94% 
96% 
Table 2. Results of change detection from decision tree 
classification for the 2 km 2 Heathrow test site. 
In the decision tree results (Table 2) there are a large number of 
false positives, in which objects which haven’t actually changed 
are falsely identified as changes. These errors are caused by a 
variety of factors. One factor was that the change detection 
works on classified features, rather than individual pixels. In 
order to do this, the groups of pixels classified by the decision 
tree had to be grouped into contiguous areas and converted to 
vector form. The process of grouping and vectorising 
inevitably leads to a slight degradation in the quality of the 
results. A second factor is the misclassification of an area of 
active construction as a building. In the construction site, 
earthworks for a new road were of a similar spectral nature to 
the buildings, and were elevated above the average ground 
surface height, making them similar in height to the building 
objects nearby. Other false alarms were caused by the presence 
of large vehicles and shipping containers - features which were 
much in evidence in this area of active construction work. 
Demol 
New 
Total 
Actual Changes 
34 
17 
51 
Objects classified as changes 
79 
96 
175 
Actual changes correctly 
classified (True Positives) 
33 
16"* 
49 
Non-changed objects classified 
as change (False Positives) 
46 
84 
130 
Actual changes not classified as 
changes (False Negatives) 
1 
1 
2 
% Classified as changes that 
were actual changes 
42% 
17% 
28% 
% of actual changes classified 
as changes 
97% 
94% 
96% 
Table 3. Results of change detection from object-based 
classification for the 2 km 2 Heathrow test site. 
Of the 161 objects flagged as new builds, 11 contained actual 
changes, but this included 16 individual new buildings as 
many were close together and so were merged in the 
classification 
* Of the 88 objects flagged as new builds, 12 contained actual 
changes, but this included 16 individual new buildings as 
many were close together and so were merged in the 
classification