In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
278 
no longer vivid) covers the ground so densely that its spectral 
signature is close to bare soil. The time when the crop looks like 
grass (shortly after gestation) has to be avoided by not using 
images acquired during this time. 
object was classified as ‘grassland’. Otherwise it will be 
rejected and classified as an error in the data base. The 
classification and the verification of the test objects are carried 
out independently from each other. 
3.3 SVM Classification and Verification of GIS Objects 
4. EVALUATION 
The SVM classifier is a supervised learning method used for 
classification and regression. Given a set of training examples, 
each marked as belonging to one of two classes, SVM training 
builds a model that predicts whether a new example falls into 
one class or the other. The two classes are separated by a 
hyperplane in feature space so that the distance of the nearest 
training sample from the hyperplane is maximised; hence, SVM 
belong to the class of max-margin classifiers (Vapnik, 1998). 
Since most classes are not linearly separable in feature space, a 
feature space mapping is applied: the original feature space is 
mapped into another space of higher dimension so that in the 
transformed feature space, the classes become linearly 
separable. Both training and classification basically require the 
computation of inner products of the form i>(fj) T • <3>(fj), where 
fi and fj are feature vectors of two samples in the original feature 
space and <J>(fj) and 3>(fj) are the transformed features. These 
inner products can be replaced by a Kernel function K(f h f)), 
which means that the actual feature space mapping <I> is never 
explicitly applied (Kernel Trick). In our application we use the 
Gaussian Kernel K(i h f ; ) = exp(-^ II f, - fjll 2 ), which implies that 
the transformed feature space has an infinite dimension. The 
concept of SVM has been expanded to allow for outliers in the 
training data to avoid overfitting. This requires a parameter v 
that corresponds to the fraction of training points considered to 
be outliers. Furthermore, classical SVM only can separate two 
classes, and SVM do not scale well to a multi-class problem. 
The most common way to tackle this problem is the one-versus- 
the rest-strategy where for each class a two-class SVM 
separating the training samples of this class from all other 
training samples is trained, and a test sample is assigned to the 
class achieving the highest vote from all these two-class 
classifiers (Vapnik, 1998). 
For the classification process in our approach, the SVM 
algorithm needs to learn the properties of the classes to be 
classified, namely the classes ‘grassland’, ‘tilled cropland’ and 
‘unfilled cropland’. The training is done using a set of objects 
with known class labels. The class labels are assigned to the 
training objects interactively by a human operator. In a first step 
a feature vector consisting of the spectral (6), textural (4) and 
structural (5) features defined in Section 3.2 is determined from 
the image data for all the training objects. Hence, the overall 
dimension of the feature vectors is 12. Each feature is 
normalised so that its value is between 0 and 1 for all training 
objects. Then, the feature vectors of all segments are used to 
train the three SVM classifiers required for the one-versus-the 
rest strategy. 
In the classification itself, the feature vector is determined for 
each test object, and it is normalised using the normalisation 
parameters determined in training. The object is classified using 
the previously trained SVM classifiers into one of the classes 
‘tilled cropland’, ‘unfilled cropland’ or ‘grassland’. However, 
for the process of GIS verification, the separation between tilled 
and unfilled cropland is meaningless. Hence, for the verification 
process, a cropland GIS object will be accepted (and classified 
as ‘correct’) if the object is classified as ‘tilled cropland’ or 
‘unfilled cropland’. Otherwise it is classified as an error and 
thus rejected. A grassland object is verified as correct if the 
In this section, we present the evaluation of our approach using 
a pan-sharpened IKONOS scene in the area of Halberstadt, 
Germany, acquired on June-18, 2005 and having a ground 
resolution of 1 m. The reference dataset is based on ATKIS. 
However, according to the ATKIS specifications, any cropland 
or grassland object may actually contain areas corresponding to 
another class as long as certain area limitations are met (AdV, 
2010). In this work, we assume each GIS object to correspond 
to exactly one of the classes. Furthermore, both for training and 
for the evaluation we have to distinguish unfilled cropland from 
tilled cropland, information that is not contained in ATKIS. The 
original ATKIS database was thus modified for our tests: each 
ATKIS cropland or grassland object consisting of units 
corresponding to different classes was split manually into 
individual objects corresponding to a single class. All the 
cropland objects in the resulting GIS data set were classified 
manually into tilled vs. unfilled cropland according to a visual 
inspection of the images. Finally, GIS objects smaller than 
5000 m 2 were discarded because we cannot assume the 
structural approach to work with such small objects. Of the 
remaining GIS objects, less than 50% were used for training, 
whereas the other objects were used for the evaluation of our 
method. As the original data base did not contained any errors, 
we changed the class label of about 10% of the test objects that 
were chosen randomly. Figure 4 shows the test scene with 
super-imposed GIS objects. The numbers of objects used for 
training and evaluation as well as the number of errors added 
for testing the verification approach are summarised in 
Table 1. 
class 
training 
test / errors 
‘grassland’ 
32 
89/8 
‘tilled cropland’ 
165 
223 / 23 
‘unfilled cropland’ 
11 
21/2 
£ 
208 
333/33 
Table 1. Objects used in the training and test datasets. 
In the training phase we fixed the maximum training error v to 
v= 0.1%. The parameter yof the Gaussian Kernel was fixed at 
y= 0.01. The training results were used to classify the test 
objects. In order to evaluate the classification process, the 
results of classification were compared to the reference. Table 2 
shows the confusion matrix of the classification results, whereas 
the completeness and the correctness of these classes are 
presented in 
Table 3. 
algorithm 
ref. 
‘tilled 
cropland’ 
‘unfilled 
cropland’ 
‘grassland 
£ 
‘tilled c.’ 
176 
0 
47 
223 
‘unfilled c’. 
1 
12 
8 
21 
‘grassland’ 
3 
0 
86 
89 
£ 
180 
12 
141 
333 
Table 2. Confusion matrix of the test objects. 
The confusion matrix in Table 2 shows that our approach does a 
good job in separating tilled cropland from untilled cropland, 
but the separation of both cropland classes from grassland is 
very uncertain. Since tilled and untilled cropland can be