as the following 
(2) 
need to deal with 
. The constant C is 
ated on the wrong 
the shape of the 
rectly affects the 
ibove linear SVM 
eparable cases. For 
tied into a higher 
ir mapping 0. The 
lewly transformed 
but only the kernel 
(3) 
(4) 
n is used (Vapnik, 
(5) 
des the distance of 
/ing a rule image, 
ition result, 
y classifier. Hence 
solve multi-class 
\A) approach, a set 
iss from the others, 
etc. The maximum 
¡termines the final 
(OAO) strategy, a 
one for each pair- 
urban. As done in 
assifier provides a 
ich, each pixel is 
•er of votes. 
SAR data multispectral image 
7^7 X 1 X 
fmal 
land cover classification 
Figure 1. Schematic diagram of the of the fusion strategy 
3. DATA SET AND PREPROCESSING 
The multisource data for the experiments contains a set of 
multitemporal SAR data and a Landsat 5 TM image from May 
2005. The SAR data includes Envisat ASAR alternating 
polarization and ERS-2 imagery from different swaths and 
polarizations (Table 1 and Figure 2). 
Sensor 
Image characteristics SAR data 
Date 
Swath 
Polariz. 
Orbit 
ASAR 
12-Apr-05 
IS 6 
HH/HV 
Asc 
ERS-2 
21 -Apr-05 
- 
VV 
Des 
ERS-2 
26-May-05 
- 
W 
Des 
ERS-2 
30-Jun-05 
- 
VV 
Des 
ASAR 
13-M-05 
IS3 
HH/HV 
Asc 
ASAR 
22-Jul-05 
IS 7 
HH/HV 
Asc 
ERS-2 
4-Aug-05 
- 
VV 
Des 
ASAR 
14-Aug-05 
IS 2 
HH/HV 
Asc 
Table 1. Multitemporal SAR data 
4. METHODS 
In the experiment eight land cover classes were considered: 
Arable crops, Cereals, Forest, Grassland, Orchard, Rape seed, 
Root crops and Urban. One hundred samples per class were 
selected from the ground mapping by equalized random 
sampling, guaranteeing that all 8 classes are included in the 
sample set. These training set was employed for both stages of 
the classification strategy, the pre-classification and the fmal 
fusion approach. Again using equalized random sampling an 
independent validation set with 500 samples per class was 
generated. 
The SVMs were applied separately on the SAR data and the 
multispectral image, following the OAO strategy. The OAO 
concept was preferred, because first tests showed differences in 
terms of accuracy after the data fusion. The training of the SVM 
with a Gaussian kernel and the generation of the rule images 
were performed using imageSVM (Janz et al., 2007), which is a 
freely available IDL/ENVI extension that employs LIBSVM by 
Chen and Lin (Chen and Lin, 2001) for the training of the SVM. 
The best parameters for y and C were selected from a user 
defined range of possible parameters based on 10-fold cross 
validation. 
The SVM fusion was performed applying imageSVM with OAO 
strategy and majority vote to the first sets of rule images. In 
addition to the SVM classifier, three different methods were 
applied to the imagery. A maximum likelihood classifier (MLC), 
a decision tree (DT) and a boosted decision tree (DT boost). 
The DT is based on the algorithm C4.5 (Quinlan, 1993). 
Boosting is a well-known method for generating a classifier 
ensemble, which is based on adaptively changing the 
distribution of the training samples during the classifier training. 
In the initial phase, all samples are equally weighted. 
Afterwards the weights of the samples are modified and 
misclassified samples assigned a higher weight than those 
classified accurately. The next DT in the classifier system is 
trained on the newly distributed samples. In our experiments, 
AdaBoost.Ml (Freund and Schapire, 1996) is used. 
t sensor sources is 
; a set of SVMs is 
rule images are 
mages is fused by 
containing all rule 
Figure 2. Subsets of SAR (Apr-2 l/May-26/Jun-30) and 
Landsat 5 TM data 
The nearly flat study site is located near Bonn, Germany. The 
region is dominantly used for agriculture and characterized by 
typical spatial patterns caused by differences in the phenology 
of planted crops. An extensive ground truth campaign was 
conducted in summer 2005. The ground truth information is 
used as reference for the generation of training and validation 
data sets. 
5. RESULTS 
The accuracy assessment (Table 2) shows that the DT achieves 
the lowest classification accuracies, irrespective of the image 
type. But a boosted DT outperforms the MLC in terms of the 
overall accuracy. The SVM applied on both data sources further 
improves the single-source results, and outperforms the other 
classifier algorithms. Nevertheless the boosted DT performs 
better in terms of accuracy when classifying the multisensor 
imagery.