International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXX V, Part B7. Istanbul 2004
Due to the spectral similarity of several classes (see Figure
5), potatoes are manly classified as stubble-fields, canola
mainly as stubble-fields or potatoes, and extensively used
grassland as maize or intensively used grassland. Sugar beet
is often found as maize or grassland, bare soil as stubble-
fields or potatoes and the discrimination between the two
canola variations was not successful, either.
The object-oriented classification approach has the
advantage to prevent from the “salt-and-pepper”-effect as it
can be observed in pixel-based classification approaches
(e.g., SAM, Figure 3). Nevertheless, if the developed class
hierarchy is instable, whole segments (groups of pixels) and
not only a pixel are misclassified which results in a low
accuracy compared to the ground-truth data. This fact has to
be taken into account while interpreting the results.
Classes such as intensively used grassland, maize, sugar
beet and canola crops can easily be classified by using
samples and manually defined membership functions.
Classes with similar spectral characteristics, as illustrated in
Figure 5, can not be reasonably separated. Therefore, the
classes potatoes, stubble-fields and soil are consolidated
into a parent class, as well as the classes canola and canola
variation. Table 2 shows the accuracies for the object-
oriented classification method. ;
Intensively used grassland is often misclassified as sugar
beet and vice versa. The same misclassification ocurred
between canola crops and low vegetation crops. Their
spectral reflectances are similar, as can be seen in Figure 5.
Since there are only 12 pixels of extensively used meadows
available in the ground-truth data, and all misclassified,
accuracy is zero. They are either classified as maize or
intensively used grassland.
Land Use User Producer Inclass
Type Accuracy Accuarcy Accuracy
Maize 0.9068 0.6114 1.3544
Intensively 0.3978 0.6167 0.4684
used grassland
Low
vegetation | 0.8712 0.5413 1.0050
crops
Canola crops 0.0421 0.1026 0.0317
Sugar beet 0.0869 0.6667 0.0899
Extensively 0.0000 0.0000 0.0000
used grassland
Overall 0.5402
Accuracy
Kappa 0.3857
Accuracy
Table 2. eCognition classification accuracies determined on
a pixel-by-pixel basis for the distinguishable land
use classes.
870
Urban areas
Intensively used grassland
Extensively used grassland
Canola & Canola variation
Maize
Sugar beet
Potatoe & Stubble-fields & Soil
Others
Figure 4. Land use classification result based on object-
oriented classification method with eCognition.
4.2 In-field Variation and LAI Estimation Results
In Figure 5, mean reflectance data from HYPERION and the
+1 standard deviation of the data from the mean for
representative fields of the various land use types present in
the area under investigation are given. The spectral in-field
variation, as a wavelength dependent percentage of zl
standard: deviation of the data from the mean, is shown in
Figure 6.
Figure 5. HYPERION spectral data of the main land use types
present in the Limpach Valley test area. The mean
reflectances of representative fields are given as
solid line, the reflectance of € 1 standard deviation
of the data from mean is shown as dotted lines.
Green LAI variations within two selected fields of sugar beet
and late stage potatoes are determined based on the WDVI.
The fields’ infra-red reflectances at 760 nm and red
reflectances at 670 nm are used, together with literature
values of a and p, (A4) for sugar beet and senescing
potatoes.
The spectral variation of the two cultivars does not exceed
10% in the VIS/NIR region of the spectrum (see Figure 6).
The resulting LAI variations are given in Table 3.
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