The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
grow/prune-back strategy in which the decision tree is allowed 
to grow to a high level of complexity, and is then pruned back 
to a more manageable level. This approach helps prevent the 
classifier getting stuck at a local optimum point too early in the 
classification process. 
2.5 Object-based classification 
An object-based classification was adopted by the third method, 
using Definiens software. This object-based method offers 
some advantages over the traditional pixel-based approach, in 
that, in addition to the spectral data, it can incorporate shape, 
texture and local context into the classification process (Benz et 
al, 2004). Also, by segmenting the image into homogeneous 
objects prior to classification, this approach helps to reduce the 
spectral variability within each class. 
The first stage of the classification process was the 
segmentation of the image into homogeneous objects. 
Definiens employs a region-merging segmentation method, 
starting with individual pixels and merging adjacent regions 
until a user-defined threshold of heterogeneity is reached (Benz 
et al, 2004). After the initial segmentation, the objects were 
classified using a hierarchy that divides the objects into 
increasingly refined categories, using user-defined membership 
functions. The membership functions are determined by 
observation, i.e. by manual examination of the characteristics of 
the objects of interest in the image. Although this is a slow 
process, it should only be required once, in the expectation that 
the objects of interest in other images will have similar 
characteristics. Earlier splits were simpler and more reliable 
(e.g. vegetated objects were split from non-vegetated objects 
using only the Normalized Difference Vegetation Index, NDVI, 
with very high reliability). Subsequent classes became 
progressively more difficult to separate and the reliability of the 
classification decreased. Spectral properties, shape and texture 
features were all used in the identification of buildings. 
Shadow was also used to constrain the buildings class to those 
objects within a defined distance from a shadow object. This 
methodology is discussed further in Sanchez Hernandez et al 
(2007). 
3. CLASSIFICATION RESULTS 
3.1 Classification accuracy 
The first test of the classification methods used a 300 m by 
270 m subset of the Heathrow dataset. The aim of the test was 
to determine the accuracy of each classification, in order to 
assess its usefulness in a subsequent change detection step. A 
training set of 600 pixels was selected, with 50% of the pixels 
in the buildings class and 50% in the non-building class. A 
further set of 200 randomly selected pixels formed the test set. 
The overall accuracies of the test pixels are shown in Table 1. 
The first set shows the accuracy achieved using only the 
spectral components of the image, while the second set shows 
the accuracy achieved when the digital surface model was also 
included in the source data. As can be seen, the DSM data 
increased the accuracy of the classification in all cases. This 
was as expected, since the buildings can be spectrally very 
similar to the surrounding man-made roads and other surfaces; 
while the difference in height distinguishes them immediately 
from the ground surface. 
Classification 
SVDD 
Decision Tree 
Object-Based 
Spectral only 
70.0% 
73.0% 
76.0% 
Spectral + DSM 
85.0% 
90.5% 
91.0% 
Table 1: Overall accuracy of the classified Heathrow subset 
For the object-based classification, a slightly different method 
was applied when the DSM data were included. First, the 
image was classified by a user-defined sequence of processes 
within Definiens. The main features used in this classification 
were shape, slope, and height in context to neighbouring 
features. The spectral information used in the process was of a 
lesser importance and was limited to the normalised difference 
vegetation index (NDVI) and the brightness. These were 
chosen since other spectral features are likely to differ 
significantly between images captured in different areas, at 
different dates and in different lighting conditions. Localised 
differences in height between building features and the 
surrounding land are less likely to differ between images and 
these should prove more reliable. It is thought that this will 
allow the process to be generalised and will therefore increase 
the potential for transferring the rule-set to different images. 
The results on a larger test area were similar to those shown in 
Table 1. As with any classification process a certain amount of 
misclassification is inevitable. In the per-pixel classifications, 
the main reasons for misclassification were the presence of 
objects on the surface which could not be distinguished from 
the surrounding buildings. These objects included large 
vehicles and shipping containers, which are similar in height 
and area to small buildings. A further cause of mis 
classification was the use of the DSM data as absolute height 
values (rather than heights relative to the surrounding pixels). 
Using the absolute heights works well in a flat area, but in areas 
of undulating terrain there will be problems between rooftops 
and man-made surfaces at the top of slopes. 
In the object-based classification, vehicles and containers were 
also often misclassified as buildings. This classification, 
however, could distinguish successfully between rooftops and 
manmade surfaces, by using the local slopes around each of the 
objects. Some misclassifications still remained, due to very 
low-rise buildings such as sheds and garages, which were not 
recognised as buildings. This occurred because these objects 
failed to meet the height and area thresholds within the rule-set. 
This was expected to occur because the rule-set was devised to 
meet the Ordnance Survey specification for significant changes 
(“Category A” changes). Category A includes newly built 
residential buildings and demolitions, but small, non-residential 
buildings are not within this specification. A Definiens rule-set 
could be constructed to detect these small buildings, but this 
would inevitably lead to misclassifications of objects of a 
similar size and height, such as vehicles. In the end, a trade-off 
must be made between the proportion of building features 
correctly identified as buildings (true positives) and the 
proportion of non-building features that are misclassified (false 
positives).