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Picture 5: zoom into a result of a hard classification 
Snow extension is calculated in the following way: 
UJ 0% snow 
IÉÉ 20% snow 
Q 50% snow 
ill 80% snow 
1Ü1 00% snow 
Picture 6: zoom into a map of snow presence obtained by 
a soft classification 
Snow area — pixel size • Npj xe | 0 f snow class 
where: 
Snow area is the snow-covered area estimated. 
Pixel size is the terrain area corresponding to the 
sensor IFOV. 
Np^i o* snow class is the number of pixels assigned to 
snow class by the classifier. 
Snow covered area extension can be valued regarding to 
snow pure pixels only and so it is underestimated 
because ali partial contributions are neglected. 
In this supervised Fuzzy-statistic classifier does soft 
classifications. This classier requires pure training set 
depicting pure situations and mixed training set depicting 
image mixture too. Mixed pixels true composition is hard 
to be valued because sub-pixel information is required. 
The simplest way to evaluate mixture composition is to 
overlay a contemporary higher spatial resolution on the 
image to be classified. The overlap of different spatial 
resolution images is a hard topic because the same item 
is represented in different ways that are hard to agree. 
Reference mistakes are very dangerous because they 
produce wrong composition evaluations and sc wrong 
training set. Next picture 7 show' the same area in two 
different spatial resolution images. 
5. SNOW COVERED AREA EXTIMATION BY 
AUTOMATIC SOFT CLASSIFICATION 
Automatic soft classifiers are able to describe mixture 
because they define membership degrees of all image 
pixels to ail the classes. Each membership degree 
represents how a class is present into a pixel. Empirical 
test showed that membership degrees are link to the 
percentage of presence of a class into a pixel. In this way 
pure pixels have one membership degree equal to 1 and 
ail the other ones equal to 0, the mixed pixeis instead 
have more than one degree greater then 0 and their sum 
is 1. A soft classification does not produce a single 
thematic map but as maps as the number of classes and 
each map describes the presence of single class by 
different percentages, in this way soft classifiers are able 
to correctly describe transition zone since they are able to 
solve mixture problem. 
Next picture 6 is a zoom into a map of snow presence 
obtained by a soft classification