rn part
'egion)
2d. The
above
t-2
several
ale and
se of an
odology
he data
alysis is
hod
The first two steps are the same used for the flooded
areas assessment (§5); then, the analysis is split into two
different methods: the first is related to image
classification, while the second is related to feature
extraction techniques.
a) First methodology. In the post-flood image a
radiometric range that characterises the different
known landslides has been determined, forcing all the
other Dns to a null value. In the pre-flood image the
same area Dns have been then recorded; a difference
between these two new synthetic images has pointed
out the existence of landslides caused by the heavy
November precipitation. In order to reduce the
radiometric noise generated by the extraction of non-
landslide features that show a similar reflective
behaviour, a general consideration has been
deducted. The majority of the slides in that area
occurred in N-W dipping, with an angle dip in a range
comprised between 6°-15° with respect to the
horizontal plane. Thus, a clivometric model has been
generated determining the dipping plane as the
projection on the horizontal plane of the interpolating
normal one, which was previously calculated for each
slope angie. The new image locates the dipping
direction in each cell. An automatic procedures has
then been implemented in order to verify the three
restrictions previously determined:
-—
the radiometric range;
2. the slope range;
3. the N-W dipping.
b) Second methodology. The aim of the second approach
is to perform a feature extraction (from the geometric
and radiometric point of view) based on landslides of
known morphologic characteristics. The suitability of
this methodology is based on some basic
assumptions:
e the majority of the slides occurred in a N-W
direction;
e the slides presents a series of parallel fractures
perpendicular to the flow direction (SW-NE).
An automatic feature extraction procedure has thus
been implemented based on a target (both extracted
from the real images and synthetically generated) that
reproduces those morphologic characteristics. The
algorithm adopted is based on the restitution of the
correlation coefficient (R) calculated on the target and
search area; the correlation coefficient is considered
acceptable when R»0.75. The output image is
composed by null values when the restriction over R
is not satisfied, where the original values are restored
when satisfied. This method allows one to determine
similar features ever where not directly visible
(shadowed slopes), because the correlation index is
independent from the original reflective values, and
takes only the morphology of the subset (Dns
geometric arrangement) into account. The different
synthetic images generated (one for each target)
using the above mentioned algorithm have then been
added, and the resulting image has been substituted
to the radiometric range image (1.) used in the
previous methodology. In fig. 6 a colour composite
(converted B/W values) of the resulting image is
shown.
69
7. FINAL REMARKS AND FURTHER DEVELOPMENTS
The extraction of flooded areas when compared to
plotted ones, shows a remarkably correspondence;
further developments concern the usage of ERS-1 Sar
images, that have not yet been integrated in the model
because of the extreme difficulty to fit them to the
absolute georeferenced one (discrepancies grater than
1.5 the ground resolution).
With regards to landslide assessment, the two
methodologies presented show a good description of the
phenomena. Further developments will regard the
integration with multispectral remotely sensed data (such
as Thematic Mappers sets) in order to evaluate possible
correlation with humidity (extracted by TM5 band) and
vegetation index (extracted mainly from TM4 band).
f 4 : ur
: D 7
"4 a > »
t pans x
z eM ae
wow f
' T x = £
2a ; ja z *
^ ww
ef ow af
Figure 6 - B/W composite showing the final image;
circled areas are recognised slides, while the black
values remaining are noise (less than 20-25%).
8. ACKNOWLEDGEMENTS
This research has been conducted in collaboration with
Prof. John Mc. Mahon Moore and Dr. Philippa Mason of
the Centre for Remote Sensing at the Imperial College of
Science and Medicine in London. The majority of the
analysed images have been provided by C.S.l. Piemonte
in Turin; without their aid the experimental image
processing could not have been done. A special thank to
everybody at the Dipartimento di Georisorse e Territorio
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996