value to know whether it would be vegetation. The red to
orange areas, which lack of blue homogeneity and green
vegetation, is more likely the concrete covered areas and
damage areas, if working on a post-disaster image. This is
particularly true for the area at the top-left of our study area. In
the case that a pre-event image was available, it would be able
to detect the wash-away areas and possibly wet areas due to
tsunami attack via change detection.
Figures 5 and 6 present the results of fine level processing of
two selected areas, in which the extracted features presented in
their identification numbers (ID) and their ‘class’; the ‘class’
here is the combined results from pixel-based spectral,
morphological, shape indices as a result of multi-criteria
evaluation. The colour code for ID is just to discriminate the
adjacent ones. Two clusters with the same colour but not next
together do not have the same ID number. Figure 5 explores
further the details of a damage area whereas Figure 6 presents
the result from the non-damage area full of old rooftops.
Figure 6. Fine level processing results of a non-damage area
Combining the evidences from both coarse and fine levels, a
disaster-induced damage area can be confirmed. Current
satellite spatial resolutions are unable to report detailed damage
information at building level but only can delineate the non-
collapse buildings and debris areas. The damage ratio is then
computed approximately. Existing method prefers the pixel-
based computation on a grid-based form, i.e. the ratio of number
of collapsed pixels to total number of pixels in a cell. With the
coarse level clusters by the developed solutions, the
approximate damage ratio would be better and probably more
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
easy to use in practice. One of the main reasons is that the street
network is usually the boundary of administrative units and also
clearly presented in satellite images, which lead the merging to
follow.
Accuracy assessment of detected information from satellite
image remains a big challenge though remote sensing images
have been employed in disaster management for decades. It is
mainly due to the gap between what remote sensing can produce
and what the disaster management practitioners demands and
get used to. There has been also a discussion on how damage
information should be presented. Consequently, previous
research (Gusella et al. 2005, Stramondo et al. 2006, Vu and
Ban 2010) faced the difficulty in comparison with the ‘ground
truth" information.
In this paper, the detected buildings are simply crosschecked
with the visually detected ones. It showed that as Figures 4, 5
and 6, the extracted results were reasonably matched with the
reference ones. Most compact objects, more likely to be
building rooftop, were well detected. Visually, the old house
rooftops in the study area are not distinguishable from the
surrounding implying that it would be tough for an automated
processing as illustrated in Figure 7. The occlusion by the trees
nearby also cleared a possible separation line between 2 objects
introducing omission errors. More quantitative assessment will
be reported in a mutual acceptable form with disaster
management practitioners.
True Colour Composite False Colour Composite iD
Figure 7. Difficult situation for automated recognition
4. CONCLUSION
Dual-scale processing framework has been introduced to
support the rapid damage estimation at the early stage after a
disaster. The initial development is to serve as part of a system
for tsunami disaster damage estimation while its ultimate goal is
to serve as early damage estimation solution for multi-type
disaster in support of emergency responses and to distribute for
detailed damage assessment. The test with QuickBird image of
Ban Nam Ken, Phanga, Thailand produced a reasonably good
result.
The result from coarse level delineated the highly suspected
damage areas and produced the focused boundaries for fine
level processing. The fine level processing designed as a semi-
automatic approach then helps to explore the damage areas in
further details and detect the non-collapsed buildings. The
combination outcomes from both levels would enable the
derivation of better damage ratio index. The solution was
designed aiming at a parallel implementation, and detailed
report of the computation time will be reported in next
publication. However, to suit the available platform of various
users, different way of implementation will be considered
including multi-core CPU, GPU and grid platform. It is
recommended to develop a suitable method for accuracy
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