EuroSDR Projects - Host Ireland

4.1.3 Classification
Figure 4-6: Segmentation level 2
The aim of the classification is to identify basic land cover types. The following classes were defined for the
Austrian data set: Vegetation (further divided into forest and meadow), shadow, water, fields, bright objects, red
roofs, grey roofs and other urban objects. The features used in the classification were selected in such a way that
they can be easily adjusted to fit new data sets. Streets are not an individual class but are classified as one or more
of the urban classes. At the lowest level the class hierarchy could be limited to the classes vegetation, shadow,
fields, urban and water. Preference, however was given to a more detailed classification, as this allows a subsequent
refinement of the classification such as the correction of fields wrongly classified as red roofs.
As described above a rule-based classification using fuzzy functions was selected. Table 4-3 gives an overview of
the types of features used for each class. Depending on the type of land cover present in an image the class hierarchy
has to be adjusted e.g. to account for water bodies. The parameters were chosen in such a fashion as to allow an
easy adjustment to data provided by different sources. Depending on the radiometric correction performed on the
data, no or hardly any adjustments have to be done to classify images from one data set. Most classes are defined by
parameters derived from spectral values such as brightness, ratio and standard deviation. Only fields are also
defined by other features such as area and number of sub-objects. Streets were not defined as a separate class but
are assigned to whatever urban class they fall into depending on the type of surface used. In this strictly hierarchical
classification non-urban features such as vegetation and fields are classified first and if the differentiation were only
to emphasise urban/non urban objects the classification could be considered finished with the class Not Fields.
However, in order to allow refinement of the classification, remove unwanted artefacts and allow a better comparison
with the reference data, the urban classes are differentiated further. The result of the classification for the whole
study area can be seen in Figure 4-7. Large parts are covered by meadows (light green) and forest (dark green). A
number of bare fields (orange) were identified as well. Of the four urban classes grey objects (cyan) are most
abundant. Bright objects (magenta) indicate urban structure with a strong reflectance as is the case for flat roofs.
Red roofs (red) are usually single houses and can be identified very well. The remaining segments are assigned to
other urban objects (yellow). Shadow is shown in black. In total thirteen features were necessary to arrive at this
classification. Many classes could be identified by only one feature. Field is the most complex class requiring four
features for identification. A subset (see Figure 4-8) shows the high detail of the classification, going down to single
building level. The next step is the comparison with the DCM in order to highlight where changes have taken place.
123