# Damage Detection from SAR Imagery: Application to  the 2003 Algeria and 2007 Peru Earthquakes

Authors: Giovanna Trianni; Paolo Gamba
Journal: International Journal of Navigation and Observation
 (2008)

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## Abstract

This paper is focused on the improvement and further validation of a recently proposed approach for the joint use of radar satellite imagery of an area affected by a major disaster and ancillary data. The study was carried out at different sites on imagery of two different earthquakes occurred one in the Mediterranean coast of Algeria on May 21st, 2003, which severely affected the city of Boumerdes, and one in the Pacific Coast of Peru on August, 15th, 2007. The combination of different radar-extracted features results in very fuzzy classification of the damage patterns, far less detailed than what available using optical imagery. However, focused results using the above-mentioned ancillary data provide enough detail and precision to be comparable with them. In particular, quantized damage level at the block level is achieved at enough detail using ALOS/PALSAR data and thus validates the original idea.


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## Body

## 1. Introduction

One  of the  most important issues in disaster damage detection is
time, and map timeliness is as important as precision. As a consequence of
that, every kind of data available is used in order to provide information to
emergency operators [1]. To this aim, remotely sensed imagery can be
instrumental, as well as geographical information system (GIS) layers, printed
maps, and historical datasets. However, while the use of this kind of imagery
has been constantly growing in the past few years, image interpretation tools,
though fast and efficient even if not highly accurate, are still not used in
many applications.Damage assessment is actually a big challenge, being impossible to
get the right data at the right time. For this reason, the organizations such
as the International Charter on “Space and Major Disasters” are forced to
consider a wide range of sources. The need of rapid damage pattern estimate
requires information extraction about damages using quick and possibly
efficient approaches suited to the decided spatial scale and the available
data. So, the scale at which the analysis can be carried out is determined by
the spatial resolution of the available data; for example, while SAR data can
be useful to extract damage information only at a parcel level, it should be
possible to recognize the single collapsed buildings from HR images.The aim of this work is to understand the viability of radar
satellite approaches to damage patterns by analyzing many different disasters
all around the world, looking also at different scales of work. SAR data are
becoming widely available with more and more fine spatial resolution, and thus
with larger usability for urban area management. This improvement actually
allows a better match between the growing requests for focused analysis in
these areas (due to the concentration of population) with the enhanced
availability of dataset. Still, approaches to disaster management in urban
areas using SAR data are very limited, due to the problems in data interpretation
and the lack of automated or semiautomated tools.


## 2. Damage Pattern Estimate FROM Sar Data

In recent technical literature, some works have already suggested
that multitemporal SAR data may provide, at a proper temporal and spatial
scale, interesting information about disaster like earthquakes and floods. Most
of these works concerning earthquakes need data coming from ground surveys to
validate but also to initiate the process of information extraction. For this
reason, these strategies are very useful in order to correlate damage patterns
with ground displacements and soil properties [2, 3], or to provide precise
3D changes of the earth crusts [4], but offer very poor results in terms of
damage assessment and rapid damage mapping of an affected area. However, it is
important to say that classification and change detection methodologies solely
used cannot provide immediately usable results to the final user. Instead,
these methods integrated with some kind of ancillary data allow obtaining more
precise and understandable results. Moreover, damage analysis is almost
uniquely required in urban areas and human settlements in general, where it is
often easily feasible to collect layers of Geographic Information System (GIS)
data.In this work, we apply a technique recently proposed in [5] for the
two test cases of the Bam (Iran)
[6] and of the Golcük (Turkey)
earthquakes. The first aim of this work is indeed to show that the proposed
approach is valid in other situations and produces useful results fro damage
assessment in different areas in the world. As a second objective, this paper
reports the results of an investigations about the robustness of the approach
to the lack of some of the features originally used in the cited papers.The overall methodology of the data analysis is proposed in Figure1,
even if, for sake of brevity, we do not recall here all the details of the
algorithm. The procedure involves first of all the extraction of a suitable set
of features from the original multitemporal dataset comprising pre- and
postevent SAR imagery of the area under test. This feature set is then input to
a multiband supervised classifier, whose output is a multiple class change
detection map. Finally, a postclassification fusion step is performed at the
end of the procedure involving the use of the above mentioned ancillary data.Figure 1
Overall structure of
the damage mapping procedure.The set of feature extracted from the multitemporal SAR images of
the area under test depends on the trivial assumption that radar returns in
damaged areas are quite different than in the original “undisturbed”
configuration of the buildings. There are studies showing that urban areas show
a remarkably strong coherence in complex return values and correlation in
amplitude/intensity values during time. It is interesting therefore to use as
hint to damages the change in the complex coherence and in the intensity
correlation. In particular, intensity correlation has been tested in technical
literature for the Hyogoken-nambu (Japan)
and Bam (Iran)
earthquakes. Following the paper where this was originally proposed [2], each
(complex) SAR image of the available data sequence Xn,n=1,2,…, is prefiltered with a Lee filter, and intensity correlation ri between the ith image and the previous one in the temporal sequence is
computed according to the formula(1)ri=∑Wxixi−1−Nx¯ix¯i−1(∑Wxi2−Nx¯i2)(∑Wxi−12−Nx¯i−12), where xi=∥Xi∥ (recall that SAR data are complex values), the ∑W(⋅) notation means that computation is done for
each image element in a window W=N×X around it, and finally the mean value x¯i is similarly computed in W.Along with intensity correlation, another valuable input feature is
the difference between the logarithmic value of the mean prefiltered data
intensitydi=10⁢log⁡10(xi¯)−10⁢log⁡10(xi−1¯), and the feature set is completed by the original pre- and postevent
pointwise intensities.Of course, according to the dimension of the windowW and thus to the geographic area of
computation of the spatial features, multiple scales of analysis of the data
can be enhanced. A convenient value for the window size, according to our past
experience, depends on the ground spatial resolution of the data and the mean
dimension of a meaningful block of buildings in the human settlement under
test. In all the considered cases, a value of N between 15 and 21 is equally
valuable, when the SAR images have spatial resolution in the 10 meters’ range.The second step of the approach, as shown in Figure1, is a multiband
classification, performed in this work comparing two different approaches: a
neuro-fuzzy per-pixel Fuzzy ARTMAP (FA) classifier [7], and a contextual
classifier based on the assumption of a Markov random field (MRF) spatial model
[8]. Generally speaking, the neuro-fuzzy classifier has been chosen because of
its proven capability to  provide good results when performing a multiband per
pixel classification, while the MRF approach allows a spatially joint analysis.
Both algorithms are supervised, and as such a minimal knowledge about the
damages on the ground, their locations, and  level is required.After classification, and due to the complex interactions between
radar waves and the urban environment, either damaged or undamaged, it is very
likely that the damage classification map has a “blurred” or “fuzzy”
appearance. To improve the results, and to meaningfully focus the damage map at
a spatial scale of interest to the final user, a fusion step between the map
results and ancillary GIS data is performed. To this aim, the best results
between the two classification methods are used to make a decision, by means of
a decision fusion process, about each of the areas detailed by the GIS
ancillary information. This processing step involves a data fusion procedure
which has been detailed in [5] and in this work will be degraded to the
simplest situation, that is, majority voting. This basically means that the
most voted damage class in each block individuated by the GIS layer is considered
as representative of the whole block.Following this procedure, in next section two different test cases
will be considered, referring to very different countries in different parts of
the world. Moreover, different SAR sensors are considered, and thus different
scale of analysis and data availability. With the results of the following
section we want to stress how much the simple procedure presented here can be
helpful in real situations, and compare how much and how well different choices
of the input feature set can highlight the damage patterns in the area. The
goal of having more test cases and comparing with the originally studied Bam
(Iran) and Golcük (Turkey) cases is also a way to check for the best
combination (if any) for all of them.


## 3. Applicative Test Cases

In order to test the proposed methodology, the aim of our tests was
to consider different SAR datasets, coming from sensors on board of different
satellites. The combination of different bands of work, spatial resolution,
polarization information, availability or not of phase information was meant to
provide a method to test the robustness of the approach and find where it has
to be adapted. Moreover, the very diverse damage patterns, connected to the
original spatial urban patterns and the effect of the earthquake, make the two
test site analyzed in the following. (in addition to the 2003 Bam test case in
[5] and the 1999 Turkey case in [6] a definitely valid series of applicative
results.)


### 3.1. First Test Site: Boumerdes  (2003 Algeria
Earthquake)

The first results refer to the magnitude 6.8 earthquake occurred in
northern Algeria
on May 21st, 2003. Centered on the Boumerdes province (Figure2) some 50 km east of Algiers, the worst
affected urban areas included the cities of Boumerdes, Zemmouri, Thenia,
Belouizdad, Rouiba, and Reghaia. For this event, many different remotely sensed
data are available; in this work the analysis will be concentrated in the urban
area of Boumerdes, for which two ERS-2 have been acquired, one pre-event
acquired on July 27th, 2002, and one postevent acquired on June 7th, 2003.Figure 2
Location of the
urban area of Boumerdes in Algeria.


### 3.2. Second Test Site: Pisco (2007 Peru
Earthquake)

The second example refers to the test case of Peru, whose central coast was
stricken by a 7.9-magnitude earthquake on August 15th, 2007. Among the affected
cities, the city of Pisco
has been considered because it appears in two ALOS/PALSAR fine beam double
polarization (HH/HV) Precision images, provided in geocorrected form and 12.5
meters posting. The two images were acquired before (on August, 12th, 2007) and
after (on August 27th, 2007) the  earthquake. Ancillary data consist of a GIS
layer depicting the borders of the parcels in the urban area, and were obtained
by manual digitalization of the information in [9], and validated by comparison
with the same SPOT pre-event image used in that paper. From the same paper,
also the information related  to damaged areas obtained by in situ measurements
was extracted (see Figure 5).Since the data have been provided as amplitude images, no phase
information could have been considered. This prevents us from using the bands
which were  considered as the best choice in [5] for the problem of damage
detection, that is, pre- and postevent intensity, pre-post coherence and
difference between pre-post and pre-pre coherence. Instead, from the available
images only some intensity features have been extracted, in particular the
intensity correlation r and the
backscattering coefficient d and
taking into account the different spatial resolution of the ALOS/PALSAR scene
than the ERS and JERS data used in all the works on the same subject so far. Other
considered features are the pre- and postevent intensities, computed from the
original data after despeckling with a 5 × 5 Gamma
filter. As in the first test case the first classification by means of the
above neuro-fuzzy per-pixel classifier or the context-aware MRF classifier is
followed by a fusion with the GIS layer, where each parcel of the GIS is
assigned to the class to which the majority of mapped pixels belong.For this test case, a wider range of classification maps is proposed
in Figures6–9 to allow a
better comparison of the combinations of features and classifiers, as well as
to appreciate the improvement in understanding the results by using the data
fusion final step. The need for this extended result analysis is connected to
the different spatial resolution of ALOS/PALSAR with respect to the ERS/JERS
data used so far. This makes the classification map more precise at the
per-pixel level and allows defining spatial units smaller than in the previous
test site (compare Figures 5(b) with 3(b)). Moreover, the lack of
original complex data does not allow computing the phase coherence, one of the
most important bands for the multiband/multitemporal damage assessment
classification step according to [5]. We thus intend to analyze which, among
the computed features, are the most viable ones to get a rapid damage map in
this situation.Postevent SAR
image (left) and available damage map (right), where brown refers to highly
damaged areas, orange to medium damaged areas, and off-white to undamaged areas.
(a)(b)Damage maps for
the Boumerdes area: (a) using remotely sensed data and change detection
algorithm; (b) introducing the GIS information about parcel borders in the
urban area.Postevent
pan-sharpened image of the town of Pisco (a) and GIS information (b) about the
blocks in the town and (c) damage map with three classes (from [9]): green
(untouched), yellow (light damage), and orange (medium/high damage).
(a)(b)(c)Figure 6
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Figure 7
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Figure 8
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Figure 9
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Finally, Table1 reports the overall accuracy for the maps in the
rightmost column in Figures 6–9 and allows to
improve the visual comparison with a quantitative assessment.Table 1
Overall accuracy for maps in Figures5–9.
Overall accuracyO.A. (4 classes)Figure5 MRF + GIS56.51%45.71%Figure5 FA + GIS75.66%Figure6 MRF + GIS66.98%Figure6 FA + GIS65.08%Figure7 MRF + GIS70.16%44.44 %Figure7 FA + GIS67.62%Figure8 MRF + GIS69.84%Figure8 FA + GIS68.25%A first comment to the results is that the information fusion step
is really mandatory to achieve  results not only with a decent mapping
accuracy, but also understandable to anyone looking to the map. A second
comment is that the accuracy values are in the same range as the one reported
for the first test case, the Algeria
earthquake. Although the ground truth in the present case is more detailed, the
higher spatial resolution of the SAR data allow matching the accuracy values
obtained from Boumerdes’ images.According to the maps and the accuracy values, the best result is
obtained by using a combination of the amplitude pre-event image (both
polarizations HH and HV) and the amplitude postevent image (again with both
polarizations), and the second best approach is the use of the backscattering
and the correlation computed between the pre-event image (HH polarization) and
the postevent image (HV polarization), and the postevent image (with both
polarizations).Since the ALOS/PALSAR image pre- and postevent image pairs are alternate
polarization (AP) images, it was also possible to compare the effect of
polarization with respect to damage mapping task using the proposed approach.
However, it was found that no particular choice can be made, and the problems
in mapping the damage to the right extent in some portion of the area are
equally in place suing one or the other of the two polarizations, or even a
combination of both.A last comment is driven by the fact that the damage maps in [9]
involve four classes, instead of the three used in our validation. In Figure 9,
in fact, medium and high damages were considered as a single class, in orange.
The reason is that previous trials attempting to obtain maps with high level of
damage discrimination did fail. The highest accuracy values, as reported in
Table 1 for sake of completeness, were around 45% at their best. The
corresponding maps, where a high level of damage is depicted in red, are
reported in the last row of Figures 6 and 8. Apparently, there are limits
inherent to the structure of the artificial elements of the landscape, the
typology of earthquake, and the spatial resolution that
prevent the 7 m ALOS/PALSAR to
be enough for this precise recognition task.


## 3.1. First Test Site: Boumerdes  (2003 Algeria
Earthquake)

The first results refer to the magnitude 6.8 earthquake occurred in
northern Algeria
on May 21st, 2003. Centered on the Boumerdes province (Figure2) some 50 km east of Algiers, the worst
affected urban areas included the cities of Boumerdes, Zemmouri, Thenia,
Belouizdad, Rouiba, and Reghaia. For this event, many different remotely sensed
data are available; in this work the analysis will be concentrated in the urban
area of Boumerdes, for which two ERS-2 have been acquired, one pre-event
acquired on July 27th, 2002, and one postevent acquired on June 7th, 2003.Figure 2
Location of the
urban area of Boumerdes in Algeria.


## 3.2. Second Test Site: Pisco (2007 Peru
Earthquake)

The second example refers to the test case of Peru, whose central coast was
stricken by a 7.9-magnitude earthquake on August 15th, 2007. Among the affected
cities, the city of Pisco
has been considered because it appears in two ALOS/PALSAR fine beam double
polarization (HH/HV) Precision images, provided in geocorrected form and 12.5
meters posting. The two images were acquired before (on August, 12th, 2007) and
after (on August 27th, 2007) the  earthquake. Ancillary data consist of a GIS
layer depicting the borders of the parcels in the urban area, and were obtained
by manual digitalization of the information in [9], and validated by comparison
with the same SPOT pre-event image used in that paper. From the same paper,
also the information related  to damaged areas obtained by in situ measurements
was extracted (see Figure 5).Since the data have been provided as amplitude images, no phase
information could have been considered. This prevents us from using the bands
which were  considered as the best choice in [5] for the problem of damage
detection, that is, pre- and postevent intensity, pre-post coherence and
difference between pre-post and pre-pre coherence. Instead, from the available
images only some intensity features have been extracted, in particular the
intensity correlation r and the
backscattering coefficient d and
taking into account the different spatial resolution of the ALOS/PALSAR scene
than the ERS and JERS data used in all the works on the same subject so far. Other
considered features are the pre- and postevent intensities, computed from the
original data after despeckling with a 5 × 5 Gamma
filter. As in the first test case the first classification by means of the
above neuro-fuzzy per-pixel classifier or the context-aware MRF classifier is
followed by a fusion with the GIS layer, where each parcel of the GIS is
assigned to the class to which the majority of mapped pixels belong.For this test case, a wider range of classification maps is proposed
in Figures6–9 to allow a
better comparison of the combinations of features and classifiers, as well as
to appreciate the improvement in understanding the results by using the data
fusion final step. The need for this extended result analysis is connected to
the different spatial resolution of ALOS/PALSAR with respect to the ERS/JERS
data used so far. This makes the classification map more precise at the
per-pixel level and allows defining spatial units smaller than in the previous
test site (compare Figures 5(b) with 3(b)). Moreover, the lack of
original complex data does not allow computing the phase coherence, one of the
most important bands for the multiband/multitemporal damage assessment
classification step according to [5]. We thus intend to analyze which, among
the computed features, are the most viable ones to get a rapid damage map in
this situation.Postevent SAR
image (left) and available damage map (right), where brown refers to highly
damaged areas, orange to medium damaged areas, and off-white to undamaged areas.
(a)(b)Damage maps for
the Boumerdes area: (a) using remotely sensed data and change detection
algorithm; (b) introducing the GIS information about parcel borders in the
urban area.Postevent
pan-sharpened image of the town of Pisco (a) and GIS information (b) about the
blocks in the town and (c) damage map with three classes (from [9]): green
(untouched), yellow (light damage), and orange (medium/high damage).
(a)(b)(c)Figure 6
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Figure 7
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Figure 8
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Figure 9
Per-pixel damage
maps and focused damage map using ancillary information for both the MRF and FA
case. The input multiband/multitemporal dataset is detailed on the left.Finally, Table1 reports the overall accuracy for the maps in the
rightmost column in Figures 6–9 and allows to
improve the visual comparison with a quantitative assessment.Table 1
Overall accuracy for maps in Figures5–9.
Overall accuracyO.A. (4 classes)Figure5 MRF + GIS56.51%45.71%Figure5 FA + GIS75.66%Figure6 MRF + GIS66.98%Figure6 FA + GIS65.08%Figure7 MRF + GIS70.16%44.44 %Figure7 FA + GIS67.62%Figure8 MRF + GIS69.84%Figure8 FA + GIS68.25%A first comment to the results is that the information fusion step
is really mandatory to achieve  results not only with a decent mapping
accuracy, but also understandable to anyone looking to the map. A second
comment is that the accuracy values are in the same range as the one reported
for the first test case, the Algeria
earthquake. Although the ground truth in the present case is more detailed, the
higher spatial resolution of the SAR data allow matching the accuracy values
obtained from Boumerdes’ images.According to the maps and the accuracy values, the best result is
obtained by using a combination of the amplitude pre-event image (both
polarizations HH and HV) and the amplitude postevent image (again with both
polarizations), and the second best approach is the use of the backscattering
and the correlation computed between the pre-event image (HH polarization) and
the postevent image (HV polarization), and the postevent image (with both
polarizations).Since the ALOS/PALSAR image pre- and postevent image pairs are alternate
polarization (AP) images, it was also possible to compare the effect of
polarization with respect to damage mapping task using the proposed approach.
However, it was found that no particular choice can be made, and the problems
in mapping the damage to the right extent in some portion of the area are
equally in place suing one or the other of the two polarizations, or even a
combination of both.A last comment is driven by the fact that the damage maps in [9]
involve four classes, instead of the three used in our validation. In Figure 9,
in fact, medium and high damages were considered as a single class, in orange.
The reason is that previous trials attempting to obtain maps with high level of
damage discrimination did fail. The highest accuracy values, as reported in
Table 1 for sake of completeness, were around 45% at their best. The
corresponding maps, where a high level of damage is depicted in red, are
reported in the last row of Figures 6 and 8. Apparently, there are limits
inherent to the structure of the artificial elements of the landscape, the
typology of earthquake, and the spatial resolution that
prevent the 7 m ALOS/PALSAR to
be enough for this precise recognition task.


## 4. Conclusions

In this paper, a rapid damage mapping approach is proposed, based on
the exploitation and interpretation of satellite SAR data. The approach proves
to be robust and useful to detect the damage patterns. It is however imprecise
with respect to accuracy and needs improvements.More specifically, although semiautomatic SAR data interpretation in
urban areas is an open research issue, this paper shows that a combination
(fusion) of remotely sensed data and geographical databases may lead to a real
improvement in this interpretation, making the data more useful for the
end-user. Accuracy and robustness of the procedure, together with its
affordability, were proved by the analysis of extreme events (notably,
earthquakes) in many different parts of the world.There are some commonalities among the choices of input features
used in the cases presented in this work, and the combination of the pre- and
postevent intensity data with the intensity correlation and the difference of the
logarithmic means achieves
always better results. The possibility to incorporate some phase information by
means of coherence, not exploited in this work but proposed in the original
paper [5], leads apparently to the best among these multiple possible choices.Very interesting and still open issues are those connected to the
analysis of additional spatial feature and the correlation between the maps and
the features itself to the actual, on site generated, damage map.



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*Source: 762378-2008-08-26.xml*