In: Wagner W., SzSkely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010,1 APRS, Vol. XXXVIII, Part 7B 
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Figure 2. Hierarchy of classification rules. 
classification result. Some time was spent on optimizing the 
parameters, but it was felt that it was not a good idea to spend 
too much time on this, as this would have to be repeated for a 
new model. 
Agricultural land, rivers and lakes are not considered important 
in this project, as they are well mapped, and can be obtained 
from GIS. However, the positional accuracy is often lower than 
for buildings and roads. 
The result of the classification procedure was a 0.6 m resolution 
image with the following classes: 
1. Open grass land and lawns. 
2. Bushes, trees, forest. (Parts of) private gardens are 
expected to fall into this class. 
3. Little vegetation: Paths, grass areas with substantial 
wear and tear. 
4. “Grey” areas, that is, covered by buildings, roads, 
parking lots, etc; thus with no vegetation. 
5. Not classified or missing data, also used for water. 
The three first classes are regarded as “green” areas, and can be 
seen as subclasses of green areas. 
3.4 Postprocessing of classification result 
The classified image can be combined with GIS data of 
buildings and roads. Trees overlapping buildings and roads are 
kept, based on the NDVI value, but other parts of the buildings 
and roads are subtracted from the vegetation classes. 
Enhanced versions of the Oppegard and Lorenskog areas were 
created by using GIS data for buildings and roads. The houses 
and roads were subtracted from the green areas if the NDVI was 
low. In cases where the NDVI was high, for example, caused by 
a tree overlapping a house or a road, the tree was kept. 
4. VALIDATION METHODOLOGY 
The classification may be validated manually or automatically. 
In order to perform an automatic validation, a ground truth must 
be established. For Oppegard and Lorenskog municipalities, we 
have obtained digital maps, free of charge, of roads and 
buildings, for use within the project. These maps can be used to 
validate grey versus green area classification, but can not be 
used to validate which of the three green area classes that has 
been assigned. 
One major shortcoming of the digital map we had access to is 
that not all grey areas are included. Large parking lots are 
missing, as well as private driveways. So, the digital map could 
be used to find houses and public roads that were partially or 
fully missing in the automatic classification. However, areas 
that had been misclassified as grey areas could not be flagged, 
since many grey areas are missing in the digital map. 
Thus, manual validation of the automatic classification was 
needed. The intention was also that the manual classification be 
used to validate the subclasses of green areas. However, this 
turned out to be too difficult to do in a quantitative manner. 
Only some general observations could be made. Where 
available, the digital map was used to guide the manual 
validation 
4.1 Manual validation method 
4.1.1 Selection of validation area 
Given the size of the image, and the available resources for the 
project, a complete inspection of the classification result of the 
entire image was considered infeasible. Instead, a selection had 
to be made. Manual selection of areas that could be considered 
“representative” would lead to a biased result. On the other 
hand, some of the selected areas should cover the areas of 
which we had map coverage. These considerations led to the 
following selection procedure of validation areas. 
1. Set the image counters N 0p pegw, N^^g and N 0s i 0 
all to zero. 
2. Pick an x,y coordinate within the image at random. 
The range of possible values are 1 .. x max -x size for the 
x coordinate, and 1 .. y max -y S ize for the y coordinate, 
with x max , y max being the Quickbird image size and 
x S j ze , y s ize being the validation area size.