International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
for the Marmara earthquake, Landsat and IRS images were
acquired; for Boumerdes, IRS and Quickbird imagery. Satellite
and scene specifications are provided in Table 2 and 3.
satellite/sensor n°of bands resolution
30 m (band 1-5, 7)
Landsat 7/ ETM+ | 7ms+ 1 pan 120 m (band 6)
15 m pan
IRS | pan 5m pan
; : 2.8m ms
QuickBird 4 ms + 1 pan 0.7 cm pan
Table 2. Technical specifications of the sensors used.
sun off
de nadir, product
elevation,
: target level
azimut ;
azimuth
e Marmara
TMS 08.18.1999 SYSCOIT.
ETM+ 08.10.1999 SySCOIT.
IRS 08.08.1999 63.2, 152.4 1D
IRS 09.27.1999 47.0, 167.0 1D
e Boumerdes
IRS 08.12.2002 63.5, 141.0 ID
IRS 06.08.2003 69.8, 129.3 ID
QuickBird 04.22.2002 61.4 1442 |] 112,176 2A
QuickBird 06.13.2003 672 1199 1 157.278 2A
Table 3. Specifications of the scenes acquired.
3. MACROSEISMIC DAMAGE ASSESSMENT
3.1 Interpretation of satellite imagery in an assisted
manual procedure.
Using Quickbird pre- and post-event images is possible to
produce a map of damaged buildings, as required by EMS 98
macroseismic classification, at least for higher grade of
damage. However, two Quickbird images need correction and
registration to have a correspondence between single structural
units on two different images (Fig. 1).
For the Boumerdes earthquake, the pre-event Quickbird image
was subjected to a multiresolution segmentation by eCognition
software in order to obtain meaningful region/objects,
representing buildings. eCognition uses some keywords to
define how the segmentation is done: scale, color and
smoothness. Scale is the magnitude of the object, and is
different from resolution, which is the minimum feature
detectable on the image. Using the concept of scale is possible
to detect features with different magnitude, for example large
built areas or forest (high scale value) and single buildings or
trees (small scale value). The scale is defined as the value of an
heterogeneity factor beyond which two objects are fused
together. This heterogeneity factor is a weighted mean, from 0
to 1, of a spectral heterogeneity (color) and a shape factor; the
latter is composed by smoothness and a compactness factor. A
low color factor (thus a high shape factor) is used because
buildings present some irregularities on the roof (principally
dormers or saturated areas).
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Figure 1. Boumerdes, change between 2002, April 22 and 2003,
June 18.
The segmented image is used in a procedure developed in
Matlab for assisted classification. The purpose of segmentation
is double: firstly, a building can be selected without digitizing
its border, with a few mouse clicks (this is clearly an
assumption, because detecting a single structural unit in this
way is rather arbitrary). Secondly, the same feature can be
found in the post-event image and a damage class attributed by
visual inspection, obtaining in this way a database of linked
polygons on the two images, generally with a slight different
registration. In this manual classification phase, a precise
geometry isn't needed, as well as the interest is only in the
informative contents: the geometric information coming from
this classification will be used later to obtain a precise co-
registration for the automatic classification algorithms.
Results for the whole images are shown in Table 4, where the
mean of ratio between reflectance values assumed by the
corresponding polygons is also reported for each damage class,
in a attempt to find a correlation as evidenced by other authors
for lower resolution imagery (Eguchi, 2000).
Damage itas EEG RatioPAN
Class Buildings classified | (Jun2003/Apr2002)
Class0 2328 [131
Class3 12 1.186
Class4 54 1.095
Class5 100 1.124
Table 4. Results of the macroseismic object classification by
the assisted manual classification procedure.
0.4 0.4
0.3 0.3
0.2 0.2
0.1 | 0.1 |
05 À ; la... 0 AM Lo
Figure 2. Left: ratio (post/pre event) in damaged buildings
(black) and no damaged buildings (blue); Right:
reflectance increasing in building and surroundings.
As can be observed in Figure 2 (left), it is quite impossible to
define a threshold value to classify the damage using only roof
reflectance. On the other hand, photo interpreters take into
account surroundings of object/buildings to establish if a
building is damaged or not: to simulate the surrounding of
every building, a segmentation with an higher scale can be
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