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If the scatterers are regularly distributed and if they have the 
same scatter characteristic, the cell is called homogeneous. 
Small changes in structure or in chemical composition of the 
ground-cover in such a cell will not change the phase if the 
imaging geometry did not change. 
In a non homogeneous cell the dominant scatterers will 
have the highest influence on the phase therefore variation 
in the position of dominance will vary the phase 
significantly. 
Apart from temporal changes, decorrelation can also be a 
consequence of an improper (too large) distance (base) 
between the two sensors during the data acquisition. To 
reduce the baseline decorrelation a careful selection of the 
orbits of the sensor system(s) is required. For the extraction 
of topographic and thematic information different constraints 
are involved. If a data pair is used to extract spatial and/or 
height information by means of an interferogram, the base- 
line between the two orbits should range from 200 to1000m 
to realize an acceptable height resolution. In case of the 
extraction of thematic information, the decorrelation related 
to the base line should be minimized. If the decorrelation 
related to the baseline is zero, the remaining decorrelation 
is caused by temporal changes in composition and structure 
of the ground cover within the cells . 
In practice a zero baseline will not exist, but with a short 
baseline almost no baseline decorrelation exists. So 
differences that appear can be considered to be caused by 
temporal decorrelation. The data can be used as an 
additional dimension in the feature space for the combined 
image analysis. 
1.1 Neural Network classification 
Neural networks are based on a model of the human brain, 
using certain concepts of its basic structure. The network 
consists of many simple processing elements (neurons) 
ordered in layers. These layers are separated into an input, 
one or more hidden layers and an output layer. The 
elements in the hidden layer(s) are connected with all or 
with some elements in the next/previous hidden , input or 
output layer. In an operational neural network, these 
connections are weighted in a training stage. The training of 
the network is based on a set of vectors of which the class 
membership is known. The neural network classifiers are 
able to learn from sample patterns. These classifiers do not 
need a particular frequency distribution as required by some 
conventional statistical classifiers. 
1.2 SAR Coherence image 
For the creation of a coherence map, two SAR complex 
data sets have to be registered and the coherence 
computed. Coherence is a measure for the relation of the 
phase information of corresponding signals. To reduce large 
fluctuations in the map the coherence is computed in a 
window. 
According to Schwábish and Winter there are several 
factors which decrease the coherence: 
- thermal noise 
- temporal changes in atmospheric conditions 
- phase errors due to processing 
- temporal changes in the object phase 
* different viewing positions 
171 
2. DATA PREPARATION 
The "ground truth" data was collected in the field and their 
positions indicated on a topographic map. The SPOT image 
was georeferenced and geocoded to the geometry of that 
topographic map. Before the classifications were performed, 
the data sets (SPOT and Coherence Image) were 
registered. Further a neural net was initiated and trained. 
2.1 Data description 
For the experiment a data set is selected consisting of an 
optical image (SPOT-XS) and a tandem of SAR images 
(ERS-1, ERS2) in single look complex format (SLC). In 
   
    
  
Figure 1: SPOT XS band 3 
figure 1 , band 3 of the Spot image is shown. The SPOT 
image is acquired on 02 August 1995, the ERS-1 image 
(figure 2) on 19 August 1995 and the ERS-2 (figure 3) on 20 
August 1995. Because of the small time interval between 
the acquisition dates it is expected that the types of ground 
cover of the sensed area have not changed dramatically. 
The weather conditions during the data acquisition of the 
two ERS images were perfect for the experiment; without 
rain but with different wind force and direction. 
The baseline of the two images has a horizontal component 
of 38 meters and a vertical component of 82 meters 
Figure 2: ERS-1 Intensity image 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B2. Vienna 1996