International Archives of the Photogrammetry, Remote Sensing 
4. RESULTS 
4.1 Feature selection for ERS SAR-images 
4.1.1 Individual ERS-images: Figure 1 represents the average 
separabilities of land cover classes as function of average 
transformed Bhattacharyya-distance. The best separabilities 
have been acquired using images taken 5.5.1999, 16.4.1999 and 
8.10.1999, but even in these cases the average separability is 
rather low. The corresponding weather conditions have been 
full snow cover with raining wet snow, 50% snow cover with 
raining wet snow quite heavily, and it has been raining 
(Pulliainen, 2004). So, it seems that the best conditions in order 
to separate these land cover classes are wet snow or ground. The 
worst separabilities have been acquired using images 14.7.1999 
(no rain) and 27.10.1999 (some rain). Class water is the most 
separable class, the worst are agricultural field and open land. 
Median filtering of the intensity images increases the 
separability. The separabilities are higher with 25m pixel size 
than 12.5m pixel size. 
Average separability of land cover classes 
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Figure 1. The average separabilities of land cover classes as 
function of average transformed Bhattacharyya-distance, dashed 
line means that separabilities have been computed from original 
ERS-intensity images, solid median filtered intensity images, 
solid line with "x" texture feature Mean and solid line with "o" 
texture feature Angular second moment. 
In the case of tree species vs. development class, the 
separabilities between classes are very low. The best image is 
taken 14.7.1999 in dry and warm conditions. In the case of tree 
species vs. soil type, the sepa "abilities between classes are very 
low. The best image is taken 31.3.1999, in full wet snow cover 
and rainy (water) conditions. 
4.1.2 Texture features: The separabilities of texture images 
varied a lot depending on the used texture feature. The best ones 
were Angular Second Moment and Mean, their average 
separabilities as function of image are represented in figure |. 
The behaviour of the average separability is very similar than in 
the case of intensity images. In the case of texture feature 
Angular Second Moment, the most separable land cover class is 
water, the worst are pine forest and open land. In the case of 
texture feature Mean, the most separable land cover class 1s 
water, the worst pine, deciduous forest and open land. 
As the classification system is tree species vs. development 
class, the separabilities are rather low. The most separable 
classes are middle aged pine in image taken 8.10.1999 and 
spruce sapling in image taken 31.3.1999. As the classification 
and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
system is tree species vs. soil type, the separabilities are low. 
The most separable classes are pine and spruce on mineral soil. 
4.1.3 Best SAR-images: The best subsets of ERS-images were 
selected using Branch-and-Bound algorithm with average 
interclass divergence as selection criteria. The four most 
important images were taken 5.5., 16.4, 8.10. and 9.6.1999, in 
the case of intensity images and land cover classes. Two of 
these images have been taken in wet snow conditions, one in 
rainy and on in rather dry conditions. 
The most important texture features varied a lot depending on 
the classification system. The three most important features for 
land cover classification were Mean, Entropy and Standard 
deviation, and the worst was Homogeneity. The three most 
important features for tree species vs. development class 
classification were Homogeneity, Contrast and Dissimilarity, 
and the worst was Angular Second Moment. The three most 
important features,for tree species vs. soil type classification 
were Angular Second Moment, Mean and Correlation, and the 
the worst was Dissimilarity. 
4.2 Classification of MODIS-images 
Fuzzy means were calculated using weekly NDVI maximum 
images and training data for every class. The idea was that the 
fuzzy means could be used as training data in a supervised 
classification. Figure 2 represents these means for land cover 
classes. The beginning of the growing season can be seen 
during weeks 16-18 from the beginning of the year. Lower 
values during the weeks 23 and 27 are probably due to bad 
weather. 
  
  
  
    
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Figure 2. The fuzzy means of different land cover classes 
computed from MODIS NDVI-mosaics. 
The aim of the classification of MODIS NDVI-mosaics was (0 
produce the proportions of different land cover classes for each 
MODIS pixel. First, the fuzzy means were used as training data 
for Spectral Angle Mapper and Spectral Unmixing 
classifications. Unfortunately the results were quite poor. Fuzzy 
supervised classification was also carried out. After calculating 
the fuzzy means and fuzzy covariance matrix, the membership 
values for each class were computed. Results of this method 
were slightly better than previous ones. 
Due to poor results of previous algorithms, the Bayesian 
Maximum Likelihood classification was carried out. Training 
data pixels whose proportion of the main class was more than 
50 94 formed the training set of that class. These pixels were 
decided to represent absolute and single classes. The 
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