You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Technical Commission VII

/ pixels
g to the
is to be
le from
s those
iles are
ay tiles
ten are
ses and
to map
ne, the
able in
ed by
lysis of
ut map,
A class
red that
lass for
a set of
:d. This
| space
ing the
analysis feature extraction (determines a feature subspace that is
optimal for discriminating between defined classes). The output
of the extraction is a linear combination of the 125 original bands
to form new bands (features) that automatically occur in
descending order of their value for producing an effective
discrimination. Twenty two (22) features are obtained from the
feature extraction process. However, only the 11 features obtained
in the final feature extraction transformation matrix (DAFE) are
used to form a new data set since these provide most of the
available separability and this is confirmed by the magnitude of
the corresponding eigenvalues (high values). The new data is
classified using the ECHO classifier. The output classification
map is overlaid with an orthophoto covering the same area as
shown in Figure 2.
Roof material 2
Roof material 3
Zinc plated sheet
Kaufland roof material
Roof material 5
Roof material 1
Red roof chipping
Roof material 4

Roof material 6

Figure 2: Overlay of classification map and orthoimage.
The classification map fits well with the orthophoto and this gives
an indication of the accuracy of the classification in terms of
geometry. In order to identify areas in the classification map
which require improvement, the corresponding classification
probability map is inspected (see Figure 3). The pixels
represented by yellow to red colours in the probability map
indicate a high probability of being correct. These pixels are very
close to the training pixels for the classified pixels. Dark blue
colours represent a low probability of being correct. The pixels
represented by these colours are very far from the training pixels
for all the classes and are candidates for definition of additional
training regions.

Figure 3: Classification probability map.
Defining additional training regions for areas with a low
probability helps to improve the result. Most of the roofs in the
probability map with a low likelihood of being correct consist of
heterogeneous surface materials. For instance, the material of the
roof in a white circle is not homogeneous. Therefore, additional
training regions are required for areas where a surface material
varies in terms of spectral properties. Defining training regions
for areas requiring improvement is sufficient for achieving a
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
classification result that represents ground features accurately.
However, the required number of additional training regions
depends on the scene, the material classes of interest and the
accuracy requirements. The discriminant analysis feature
extraction and the ECHO classifier are applied to the whole
research area. The processing and analysis is done for each strip.
The result obtained for each strip is shown in Figure 4.

(a) Stripl (b) Strip 2

(c) Strip 3
Figure 4: Classification results of the strips covering the research
The output classification maps (Figure 4) fit well with
orthophotos covering the research area in terms of geometry.
Inspection of the corresponding classification probability maps
shown in Figure 5 indicates that most of the classified building