In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
Page 5 of 6 
600 
ruggedness of objects suggests that the segmentation step of the 
automatic algorithm has a great potential for improvement. 
Another issue is to what extent the automatic algorithm can be 
used on another Quickbird image or not. The classification rules 
in the automatic classification method have been trained on a 
subset of the image, and then evaluated on random portions of 
1000 by 1000 pixels. The illumination conditions were very 
close to ideal and uniform over the entire scene, whereas many 
other Quickbird images of Oslo have clouds. It is possible that 
the classification rules will have to be adjusted for every image 
to be processed. Also, it is not known what problems the 
presence of clouds will result in. All in all, it could happen that 
redesigning the rules is not sufficient, so that other methods 
have to be developed. 
One minor issue was dealt with wrongly in the manual 
evaluation procedure. Whenever a house or road was partly 
obscured by a tree, the tree was ignored and the house or road 
was edited to show its extent. However, in the context of green 
structure, one is more interested in the trees than in the houses 
and roads. So, some correct classifications have been marked as 
misclassifications. However, the total number of pixels that 
have wrongly been edited in this manner, is small, so the main 
findings of the evaluation are still valid. 
The smallest mapped area is approximately 100 m 2 . If for 
example there is a piece of grass land in a private garden of 10 
by 10 meters, then it will be mapped. However, if a medium to 
large tree appears in the middle, then the homogeneity criterion 
may flag the entire area as forest. 
Private gardens appear as a mixture of the three green structure 
classes in addition to the houses and driveways. Gardens also 
contain a mix of different materials in addition to vegetation, 
including furniture, trampolines, etc. In the classification rules, 
there are additional classes. Many of these are merged into the 
grey area class. In addition, there are two shadow classes, one 
for tree shadows, which are regarded as part of green 
vegetation, and one for other shadows. 
The manual editing resulted in an additional class: gravel, 
which is considered as grey area. This class was added mainly 
to meet a potential need to indicate temporary grey areas, and 
was used on construction sites. Gravel also indicates an area 
that is not sealed, permitting water drainage. However, gravel 
and sand is difficult to discriminate spectrally from concrete. 
6.1 Segmentation 
The results of the segmentation step are not directly available to 
us in the classified image, since neighboring segments in many 
cases have been assigned the same class in the classification 
step. From the classification result, it is obvious that the object 
boundaries of classified grey areas deviate substantially from 
the true outlines of houses and roads. This is especially true in 
suburban areas (Figure 3), where there are a lot of small roads 
and buildings. However, the segmentation results can be 
examined in Definience. This was done for a few selected areas. 
Level 1 segmentation often creates border segments one pixel 
wide and very long. These pixels are often a spectral mixing of 
the two neighboring regions, for example, building and 
vegetation, or at the edge of shadows. Many roads are also 
segmented into many parallel narrow and long segments. In 
other instances, the gradual transitions between different objects 
allowed segments to be merged across the true object 
boundaries. 
ESI trees IBFj grass ¡¡HI] little vegetation EH1 grey areas 
MM I I grey areas from GIS layers of roads and buildings 
Figure 3. Segmentation problems in suburban areas in Oppegard 
municipality. Top: a 330 m x 250 m part inside validation area 1 of the 
Pansharpened Quickbird image. Middle: the automatic classification 
result for this subimage, with houses and roads from a digital map 
superimposed in grey. Bottom: Aerial orthophoto of the same area, 
captured with 10 cm ground resolution. 
Figure 4. Close-up of the upper left corner of the part of the aerial 
image of Oppegard in Figure 3. 
Many of the segmentation problems are due to shadows from 
buildings (Figure 5) and trees (Figure 6). Building shadows are 
often classified as grey areas. It could be possible to predict 
these shadows from the building height and the sun’s position. 
The building height might be available from a digital map, and 
the sun’s position can be computed from the acquisition time 
and date for the satellite image.