170
Picture 2: Zoom into the map obtained by a
photcinterpretaticn. Snow covered areas are white
coloured.
Snowpack borderlines are very jagged, there is not a
clear border between snow and free soil and so it is quite
difficult to bound snowpack and then to evaluate its
extension. This way requires a iot of time, the accuracy
of the snow identification is not well-know and the
snowpack extension is empirically vaiuated.
4. SNOW COVERED AREA EXT1MATION BY
AUTOMATIC HARD CLASSIFICATION
Snow covered areas can be identified by an automatic
classification since snow has a typical spectral behaviour
different from snow free soil. Next picture 3 shows snow
spectral behaviour.
Picture 3: Snow typical spectral behaviour [D. K. Hail, J.
Martinecj
Since 80’s computer development and multispectra!
images availability allow to perform automatic
classifications of remote sensing images. Automatic
classification techniques allow to train computers to
recognise automatically snow-covered areas and so to
produce thematic maps in short time. Human experts
have only to identify some well-known areas into the
images, called training set, and after algorithms
automatically identify thematic classes according to the
information of selected training set.
Classification accuracy can be evaluated comparing, for
a weli-known area call testing set, the true situations to
the classification results. The comparison is done into a
square matrix n x n, called confusion matrix, where n
represents the number of categories. The left-hand side
is labelled with the categories true, the upper edge is
labelled with the categories evaluated and the values in
the matrix represent numbers of pixels, inspection of the
matrix shows how the classification represents well-
known area, in the matrix the sum of diagonal entries
gives total number of correctly classified pixels, column
marginal sums give the total number of pixels of each
class as assigned by the classifier anc row marginal
sums give the total number of pixels of each class in the
testing set.
Next picture 4 shows an example of confusion matrix.
Classified
snow wood rock
total
T
snow
r
wood
H
Row
om/ginais
u
e
rock
total
■
■
Cci'jmn marginate
Sum at dtagon&f
elements givps iai&i }
number of correctly
classified pixos
;
Picture 4: exempla of a confusion matrix
Automatic classifiers allow to identify snowpack in short
time and to evaluate the classification accuracy
compiling the confusion matrix.
Traditional classification algorithms are called hard
because they are able to assign each image pixel only to
one class. In this way they do not allow intermediate
state due to mixture end the mixed pixels after the
classification are forced into one class, at the most into
the unclassified class. NOAA images, (6 hours time
resolution, 1 km x 1 km pixel size and low cost) are the
most suitable for a snowpack monitoring during melting
season, but they contain many mixed pixels. Transition
between snowpack and snow free soil is recorded in the
images as a mixture zone and the traditional classifiers
do not fit the problem. Infect the overall behaviour of
mixed pixels made up by snow and wood is often more
similar to cloud then to pure snow and pure wood. These
cause misciassification errors in transition zones and so
the accuracy of snowpack identification decreases a lot
Next picture 5 shows a zoom into a hard classification
result, many pixels on the snowpack borderlines are
attributed to cloud class and so the classification
accuracy is low.
