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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
In the object-based classification, the true positives were the 
same as for the other method. The difference in the object- 
based method is in the number of false positives. Although 
there are still many features incorrectly flagged as changes, 
there are significantly fewer than in the decision tree method. 
Of the 965 buildings present in the topographic database, 886 
(92%) were detected by the method, leaving 79 predicted to be 
demolished. Of these, 46 were false alarms, caused mainly by 
low-rise buildings such as industrial sheds and small residential 
properties that were in the topographic database but were below 
the height threshold required to be classified as buildings. For 
new buildings, 16 of the 17 actual changes were identified, but 
84 other objects were flagged as new buildings but turned out to 
be false alarms. These errors were mainly from objects such as 
caravans, lorries and shipping containers being mis-identified as 
new buildings and from garages and that fall outside the 
Category A specification. 
5. CHANGE DETECTION PRODUCTION TRIAL 
The results of the change detection led us to choose the object- 
based method for further development. To test the method in a 
more realistic environment, a prototype production system was 
developed, using a combination of software already used within 
the production area and software required for the classification 
and change detection process. It was decided that the process 
would be tested on a live production job and directly compared 
with the manual process of change detection currently 
employed. 
Two test sites in Sunderland in NE England were chosen for the 
production trial, one area (site A) of 23 km 2 , the other (site B) 
of 25 km 2 The DMC imagery was processed to provide the 
inputs required by the object based classifier. These inputs 
consisted of an orthorectified 4-band image mosaic, a DSM and 
a slope map derived from the DSM. The newly-acquired 
NGATE module of SOCET-SET was used to produce the DSM 
fully automatically (i.e. without any seamline editing or other 
manual processing). The same rule-set used on the first test 
dataset was also used in the trial, to test whether the rules could 
successfully be applied to different areas. 
The object-based change detection method was applied to the 
Sunderland image data and OS MasterMap topographic data. 
This resulted in a set of polygons representing potential 
demolitions and new buildings. These were presented to the 
image interpreters using a similar user-interface to the one they 
would normally use for manual change detection. The interface 
was modified slightly to automatically direct the user to each 
potential change in turn, and zoom in to the image at that point. 
The user then compared the image to the topographic data in 
the area of potential change and clicked a button to either 
accept or reject the change. Once a button was clicked, the 
result was recorded and the user was immediately presented 
with the next potential change on the list. 
5.1 Results of the trial 
In site A, 142 potential changes were detected. Of these, 35 
were accepted as real Category A changes (representing 25% 
completeness) and just one real change was missed. In site B, 
427 potential changes were detected, of which 77 were accepted 
as genuine (18%). There were 14 real changes that were missed 
in site B, which are discussed below. In terms of time taken to 
identify the changes in the two sites, it was found that the 
application of the automatic change detection process reduces 
the overall time by 50% (compared with the manual process). 
This is a significant improvement on the manual process, and 
would be improved further by some small changes to the user 
interface, which were requested by the operators. 
5.2 Changes that were missed 
The results show that 14 of the changed features were not 
detected by the automatic process, even though the initial test 
had very good completeness statistics. The nature of the 
omissions in site B were: 
• 5 demolitions 
• 4 new buildings 
• 5 minor modifications to school buildings 
Of the 5 demolitions, 3 were small buildings which did not have 
an address in the database, and therefore were assumed to be 
insignificant, non-residential buildings. In the manual change 
detection process, these were recorded as changes, even though 
they did not meet the exact criteria to be considered as Category 
A change. This is a situation in which the human operator will 
err on the side of caution, while the automated process simply 
filters these features out. A second reason for the omissions 
also involves an interpretation of the specification, this time 
relating to minor changes to school buildings. The automated 
process ignored minor changes to existing buildings, while the 
human operator treated school buildings as a special case and 
marked up any changes in such features. This could be 
overcome by identifying schools in the topographic data and 
treating them as special cases in the automatic process. A third 
reason for the discrepancies was the time lapse between the 
imagery and the topographic data. In several cases, there were 
genuine changes between the map data and the image, but the 
map data was more up-to-date, having been updated via field 
survey since the imagery had been flown. The human operator 
can readily verify this, while the automated process cannot. 
5.3 Opinions of the operator 
When asked for his opinion of the automated process, the 
operator considered that it would halve the time taken to collect 
the change data, and that it was “a very useful tool for change 
intelligence”. To determine whether this evaluation of the 
process is widely accepted, further tests of the system are 
planned. 
6. CONCLUSIONS AND FURTHER WORK 
An automated tool for detecting changes to buildings using an 
object-based classifier has proved to be sufficiently successful 
in a research environment to be taken up in a production trial. 
In this trial, the tool was well received and was shown to 
significantly reduce the amount of time taken to identify the 
changes over a 48 km 2 area. At the time of writing, a large 
production trial is planned, which will use recently-captured 
imagery in an area which has changed significantly since the 
previous update to the topographic data. This will allow us to 
test the method in another area, and to determine its 
effectiveness and efficiency in an area with a large number of 
changes.