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 2 of 6 
597 
3.1 Segmentation 
The segmentation was done in two levels in a bottom-up 
fashion. 
The segmentation has to be a compromise between conflicting 
needs. On one hand, one would like to obtain large building 
blocks. At the same time, one would like to keep narrow 
corridors of green structure. Multiresolution segmentation was 
used, with two levels. The level 1 segmentation was based on 
the panchromatic image alone, whereas the level 2 
segmentation also used the multispectral image bands (Table 1). 
The level 2 segmentation is based on the level 1 segmentation, 
which means it is locked to the segment boundaries that were 
created in level 1. The level 2 segmentation essentially 
aggregates segments from level 1. 
Table 1. Segmentation parameters in Definiens Developer. 
level name 
Level settings 
level 1 
Ievel2 
Level Usage 
Create above 
Image layer weights 
QB PAN 
1 
1 
QB NIR 
0 
1 
QB Red 
0 
1 
QB Green 
0 
1 
QB Blue 
0 
1 
Thematic layer usage 
(not used) 
(not used) 
Scale parameter 
20 
50 
Composition of homogenity criterion 
Shape 
0.1 
0.1 
Compactness 
0.5 
0.5 
(1-w,) Color 
’sx? —< a *—» 
W, Shape <T 
w 2 Compactness 
Set weightings pitramefets 
in the Edit Process diafog box. 
Figure 1. Homogeneity criteria in Definiens Developer. The figure is 
from (Definiens 2007), page 160. 
On each level, the segmentation process iterates several times. 
In the first iteration in level one, all segments are one pixel 
each. The mutually best pairs according to a homogeneity 
criterion are found, and each identified segment pair is merged 
into a new segment. This continues as long as segments can be 
merged without breaking the scale parameter constraint. The 
scale parameter is a threshold on the homogeneity value of a 
segment, and the homogeneity value is computed as the 
standard deviation from the ideal situation. The following 
criteria can be used, in combination 
• Color: homogeneity is computed as standard 
deviation of the spectral colors. 
• Shape: divided into smoothness and compactness 
o Compactness: homogeneity is computed as 
the deviation from a compact object 
o Smoothness: homogeneity is computed as 
the deviation from a smooth object 
boundary. 
The color and shape weights sum to 1. Within the shape 
criterion, the compactness and smoothness weights sum to 1 
(Figure 1). So, the shape value of 0.1 in Table 1 denotes that the 
shape criterion has weight 10% and the color criterion 90%. By 
increasing the shape weight, the segmentation will be more 
eager to find objects which are compact and/or smooth, and less 
eager to find objects with low color variation. 
If, for a segment, the color homogeneity is, say, 12, the 
smoothness homogeneity is 48 and the compactness 
homogeneity is 60 then the weighted homogeneity (Table 1) is 
0.9x12 + 0.1x0.5x48 + 0.1x0.5x60 = 9.2 + 2.4 + 3.0 = 14.6 , 
which is below the scale threshold for level 1, so this segment is 
accepted. However, if the shape homogeneity had been set to 
0.5, then the weighted homogeneity had been 
0.5x12 + 0.5x0.5x48 + 0.5x 0.5x60 = 6 + 12 + 15 = 33 , 
which is above the scale threshold for level 1. 
In level 2, equal weight is placed on the four multispectral 
bands (blue, green, red and near infrared (NIR)) (Table 1). One 
could place a higher weight on NIR for vegetation mapping, 
and also reduce the weight of blue if there is haze in the image. 
The scale parameter indicates how large objects one is 
interested in. To find individual trees, a low value should be 
used. To segment parts of a forest, a large value is used. We are 
interested in private gardens, where trees are present but the 
pattern is less homogeneous than in a forest. So we are 
interested in single trees and groups of trees, and a value of 50 
seemed to work well. 
3.2 Classification 
The classification was done in a hierarchical fashion. At each 
level, there are competing rules, and the rule that gives the 
highest score is selected. (In the documentation, the rules are 
called membership functions (Definiens, 2007).) There is also a 
threshold for setting an object to be unclassified. This was set to 
0.1. One can set this to, say, 0.9 during training. 
The rules used on the 2008 Quickbird image are outlined in 
Figure 2, and the actual values for the thresholds should be 
adjusted for a new image. However, one may also want to use 
different rules for another image, due to different colors, 
phenological cycle, date, haze, etc. 
Both the panchromatic 0.6 m resolution and the four bands 
multispectral 2.4 m resolution information was used in the 
classification procedure. 
The classification rules are organized in a hierarchical fashion 
(Figure 2). Note that so-called soft thresholds are being used. 
This means that instead of using a simple if-test on a threshold 
value, essentially producing a sharp transition from 0 to 1, there 
is a smooth transition zone where the response goes gradually 
from 0 to 1. Then the rule with the highest score wins. The 
actual threshold values are given in (Trier, 2009). 
When working with the rules, one might add new rules or tune 
the thresholds. At the end, one has a handful of misclassified 
and unclassified objects. One may then add “cleanup rules”. Six 
cleanup rules were used, see (Trier, 2009) for details. 
3.3 Comments 
The segmentation and classification modules in Definiens 
Developer provided a means to quickly obtain a fairly good