SAR and optical remote sensing for urban damage detection and mapping: case sudies

S. Stramondo, C. Bignami
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Itlay
stramondo@ingv.it

N. Pierdicca
University of Rome “La Sapienza”
Rome, Italy
pierdicca@mail.die.uniroma1.it

M. Chini
University of Bologna “Alma Mater Studiorum”, Bolgogna, Italy
chini@ingv.it

Abstract—Remote sensing techniques have revealed a suitable monitoring tool to provide data useful for disaster studies. They allow the quick detection of damage and building collapses due to earthquakes, especially in remote areas or where the infrastructures are not well developed to ensure the necessary communication exchanges. In particular, Synthetic Aperture Radar (SAR) sensor is widely used in environmental studies due to its characteristics which allow a fairly synoptic view in almost completely weather and time independent conditions. The spatial resolution of satellite optical sensors is rapidly increasing in the last few years, reaching less than 1 meter (IKONOS and QuickBird satellites) and thus becoming a reliable tool for detecting changes of individual buildings. The combined use of optical satellite images, either at medium and high spatial resolution, and SAR data has been investigated. The results concerning the case studies of Bam and Izmit earthquakes are reported in this paper. Moreover, a comparison between the damage assessment obtained by satellite image analysis and ground survey is also shown.
I.	Introduction
During a seismic event, a fast and draft damage map of the hit urban areas can be useful to manage civil protection interventions. Satellite remote sensing can provide useful information in this field, thanks to its synoptic capability to show a big portion of lands, especially when the seismic event is located in remote and extended regions, or when the main communication systems are damaged. Both optical and radar sensors can be exploited for this application and examples are reported in the literature. In particular, SAR is strongly sensitive to surface changes and it is almost independent on meteorological conditions and Sun illumination. Strong urban damage and the collapse of buildings after an earthquake modify the observed scenario and its electromagnetic behaviour, thus a damage assessment can be performed by means of the comparison of SAR images (both amplitude and phase) before and after a seismic event. Also optical data can furnish valuable information on settlement conditions after an earthquake. 
As preliminarily explored in a previous work [1], the possible advantages of combining radar and optical satellite data has been analysed. Secondly, since different features can be used for detecting changes derived both from optical and radar data, an investigation on their potential, the information content they carry and their possible complementarily has also been addressed. Finally, we have compared the different satellite images’ features (optical and SAR) with in situ damage inventory. In particular, the results obtained for the Turkish (Izmit and Golcuk) and Iranian (Bam) case studies have been applied to generate a damage map (providing three damage level) of the Turkish city of Adapazari. 
II.	The Study Areas and Methodolggy
A.	The case studies and dataset
Two recent destructive earthquakes have been considered for this study: the earthquake occurred in Izmit (Turkey) in 1999 and Bam earthquake (Iran) in 2003. On August 17, 1999, the Western portion of Turkey has been hit by a destructive earthquake of moment magnitude (Mw) 7.4. The epicentral area has been localised in the urban and industrial region surrounding the Gulf of Izmit (Marmara Sea), about 100 km East of Istanbul. The main event has been located 9 km South-East of the Izmit city. About 120 km of surface ruptures proceeding to the West up to the city of Golcuk and offshore in the Marmara Sea [2]. The cities of Golcuk, Izmit and Adapazari suffered heavy destructions and strong damage. The SAR dataset related to the Izmit and Adapazari case studies is composed by ERS1 and ERS2 images: two pairs of tandem acquisitions before and after the event. As far as the optical data, two Indian Remote Sensing (IRS) Satellite panchromatic images, with a spatial resolution of 5.8 m, are available: two acquisitions for the Izmit case (one pre-seismic and one post-seismic) and two acquisitions for the Adapazari one (one pre-seismic and one post-seismic).
On 26th December 2003, the South-Eastern region of Iran has been hit by a Mw 6.5 earthquake whose epicenter has been located very close to the city of Bam. The damage has been concentrated in a relatively small area around Bam [3]. The ninety percent of the building of the city has been left more or less heavily damaged or totally collapsed. The satellite dataset available in the Iranian case is composed by three Advanced SAR (ASAR) ENVISAT images from descending orbits and two multispectral images collected by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor (February 23rd,  2003 and January 2nd, 2004), with a spatial spacing of 15 m. Table I summarized the dataset composition.
In order to validate the information obtained using remote sensing data, ground survey data (i.e. the ground truth) have been used. For the Izmit case a damage map related to the city 

TABLE I. 	Satellite data used for the three case studies
Case Study	Sensor type -Satellite 	Acquisition Date	Ground Resolution	
Izmit & Adapazari
(Turkey)	Optical
(only for Izmit)	IRS	pre-seismic 8/8/1999
 post- seismic 27/9/1999	5.8 m	
	SAR	ERS-1	pre-seismic 12/8/1999
 post- seismic 16/9/99	4 x 20 m
Single Look Complex	
		ERS-2	pre-seismic 13/8/1999
 post- seismic 17/9/1999		
Bam (Iran)	Optical	TERRA-ASTER	pre-seismic 2/2/2003
 post- seismic 2/1/2004	15 m	
	SAR	ENVISAT-ASAR	pre-seismic 11/06/2003
pre-seismic 03/12/2003
post- seismic 07/01/2004	4 x 20 m
Single Look Complex	

of Golcuk has been used in the validation step. It is a map of collapse ratio (AIJ and JSCE and JGS, 1999) indicating the percentage of completely collapsed buildings with respect to the total number of buildings within a city block, which has been provided by Prof. Matsuoka. Analogous map, produced by KOERI, Kandili Observatory and Earthquake Research Institute of Turkey, has been used for Adapazari. The Geological Survey of Iran produced a similar map for the city of Bam. The maps are the result of in situ inspection.
B.	The methodology
A description of the methodology is summarised hereafter. A multitemporal/multichannel co-registered dataset has been created, depending on the available data for the different case studies. 
SAR images have been co-registered by automatic software procedure. Image intensity I has been computed and in order to partially reduce the speckle noise [4], a multi-look operation has been computed by averaging 5x1 pixels. Therefore, SAR intensity images with 20 m x 20 m of geometric resolution have been obtained. When a pair of SAR images is available, the InSAR complex coherence and the intensity correlation can be derived by combining the pre-seismic pair, the post-seismic and the co-seismic (i.e. one pre-seismic and one post-seismic image) ones. The procedures applied to compute these SAR feature maps is described in [ref. IJRS]. Note that these two features hold different information concerning changes in the scene. The complex coherence is prevalently influenced by the phase difference between radar returns, a distinctive parameter measured by a coherent sensor. It is particularly related to the spatial arrangement of the scatterers within the pixel and thus to their possible displacements. Conversely, the intensity correlation is more related to change in the magnitude of the radar return. An interactive-manual selection of control points and a polynomial warping have been used to co-register optical data and to superimpose the radar on the optical images. All SAR images and derived parameters have been re-sampled to match the spatial spacing of the optical data (5m for IRS and 15m for ASTER). SAR intensities, derived parameters (complex coherence and intensity correlation) and optical images form the set of information related to each pixel (hereinafter the feature vector of dimension N).
The work has demonstrated that a useful technique to detect changes in the scene is the pixel to pixel difference between pre-seismic and post-seismic images which have been previously co-registered. The difference is computed either on a pair of optical images or a pair of SAR images. As far as ASTER data, an arithmetic average of the two visible bands has been calculated to simulate a sort of panchromatic image. The NDVI (Normalized Difference Vegetation Index) feature has been also computed from red and near infrared bands to extract the amount of green vegetation and to select urban areas with more accuracy. Before computing the optical change image, histogram equalization has been performed to diminish the effect of different illumination and looking angle [5], by selecting only those urban portions of the reference image where no changes between pre- and post-seismic data occurred.
III.	Results
A.	Izmit case study
The extensive dataset available for the Izmit test site has allowed us to perform a damage classification exercise. By simple visual inspection of the IRS images, some urban regions, where changes have occurred, have been identified. These regions have been recognised belonging to different classes: settlements built after the earthquake, buildings with decreased dimensions (probably collapsed), rubbles, subsidence (causing sinking of the shoreline), damaged (burnt) oil tanks and a sixth class have been defined as no damage urban areas. Each region has been split to generate the training and test sets of the classifier (about 50% of pixels as test set and 50% as training set). The training pixels have been used to estimate the mean vectors and the covariance matrix of the classes distributions (assumed normal) and a maximum likelihood classifier has been adopted to classify the test pixels. The results of the classification are reported in Figure 1, in terms of overall accuracy (percentage of correctly classified pixels) and Kappa coefficient (Jensen, 1986). The classification exercise has been performed by using some different combinations of the features forming the input feature vectors. We have considered six different combinations of such features:
•	COHER:  (3-dimensional feature vector) pre-seismic coherence, post-seismic coherence, co-seismic coherence 
•	CORREL : (3- dimensional feature vector) as above but considering correlation instead of coherence
•	SAR : (8-dimensional feature vector) vector COHER, vector CORREL, pre-seismic SAR intensity image, post-seismic SAR intensity image
•	OPT : (2-dimensional feature vector) pre-seismic IRS image, post-seismic IRS image
•	OPT+SAR : (10-dimensional feature vector) vector SAR and vector OPT 
•	OPT+COHER : (5-dimensional feature vector) vector COHER and vector OPT
It is evident that SAR features alone give a poor contribution to the detection of changes, mostly because of the speckle effect that degrades class separability whereas the high accuracy using IRS data alone is partially originated from the fact that the damaged training areas have been extracted by IRS itself. From Figure 1 comes out that some SAR features contribute to improve the classification accuracy when added to the optical ones. In particular, the addition of the complex coherence determines the best classification performance.
The low spatial accuracy obtained using SAR has drawn our attention to the possibility to estimate the damage level within extended areas, where speckle effects can be smoothed. This analysis has been carried out based on independent ground truth (collapse ratio map) collected during a post-earthquake survey. The different features extracted from the above mentioned remote sensing products have been averaged within each surveyed area with same collapse ratio in order to study the correlation between remotely sensed and in situ data. Figures 2 and 3 show the most interesting results, where the normalised IRS change image and the SAR intensity correlation difference (difference between the pre-seismic and co-seismic correlations) are plotted as a function of the collapse ratio. The increase of the IRS change image value as well as the increase of the SAR intensity correlation difference as function of the collapse ratio is clearly evident. The complex coherence has not given encouraging results being strongly influenced by spatial decorrelation due to large interferometric spatial baselines (about 240 m). 

Figure 1. 	Results of pixel-by-pixel classification of damaged areas in terms of overall accuracy and Kappa coefficient as function of different sets of features used in the classification algorithm.

Figure 2. 	IRS normalized change (i.e., difference between pre-seismic and post-seismic images) averaged over the surveyed areas as function of the corresponding collapse ratio.

Figure 3. 	Pre-seismic and co-seismic intensity correlation difference averaged over each surveyed area as function of the corresponding collapse ratio.
The latter causes a substantial decrease of the complex coherence masking the drop off due to the damage [6].
B.	Bam case study
Using the procedures described in section II.B the following products have been generated: 
•	Pre-seismic coherence and correlation maps from ASAR June 11th 2003 and December 3rd, 2003
•	Co-seismic coherence and correlation maps from ASAR December 3rd, 2003 and January 7th, 2004
•	Change image from ASTER February 2nd, 2003 and January 2nd, 2004
Data have been precisely superimposed and resampled to the pixel spacing corresponding to the ASTER images (15 m). Unfortunately, ASAR image pairs had very large perpendicular baselines (more than 480 m), preventing us from detecting damage levels through the InSAR phase coherence. In fact, the latter has almost the same values both in damaged and undamaged areas as the baseline is too high. On the contrary, the SAR intensity for the Bam case still revealed itself a valuable tool for a draft survey of the damage. From the analysis of the SAR intensity change image we have obtained a draft map showing the most damaged portion of the city (the brightest points in Figure 4). This result seems to fairly agree with the ground survey map [7] shown in Figure 5. The extracted contours of the building blocks with damage levels of 50-80% and 80-100% have been superimposed on Figure 4. In the Southern area of Bam a region of apparent disagreement, with overestimation of damage from SAR change, is present. Maybe, activities occurred after the earthquake and before image acquisition could have caused it, but this cannot be demonstrated. We have also plotted the difference between the pre-seismic and co-seismic intensity correlation, averaged within an area of homogeneous damage level, as function of the three damage levels: weak, medium and strong, reported in the ground truth map. As shown in Figure 6, the sensitivity to damage level is apparent, although it is less significant with respect to the Izmit case discussed above.

Figure 4. 	ASAR absolute change image: red polygons and green polygons superimposed on the change image indicate heavy damaged building blocks (80-100%) and  medium damaged building blocks (50-80%) respectively. The polygons are derived by the ICG report map, selecting only building blocks.

Figure 5. 	The ground truth map from in situ survey (ICG report, 2004) defines three level of damage: weak damage (20-50%), medium damage (50-80%) and  strong damage (80-100%).

Figure 6. 	Pre and co-seismic intensity correlation difference vs damage levels; from undamaged to heavy damage intensity correlation difference increases as expected..
The same damage classification procedure of the Izmit test case has been applied to assess the classification capability of the medium resolution data (ASTER and SAR) on a pixel base. Four classes (no damage; weak, medium and strong damage) have been selected and randomly separated into training and test pixels. Once again the results have been quantified in terms of overall classification accuracy and Kappa coefficient and they are reported in Figure 7 for different combination of features forming the following vectors:
•	SAR: (4-D feature vector) pre-seismic and co-seismic coherence, pre-seismic and co-seismic correlation
•	OPT: (2-D feature vector) pre- and post seismic ASTER images
•	OPT+SAR: (6-D feature vector) vector SAR and vector OPT
The decrease of accuracy with respect to the Izmit case can be explained considering two factors: first, the lower resolution of ASTER data and the higher spatial baseline of the interferometric SAR pairs; second,  differently from the Izmit case, where the damaged training and test areas have been extracted by IRS itself, the damage classes extraction and the 

Figure 7. 	Results of pixel-by-pixel classification of damaged areas in terms of overall accuracy and Kappa coefficient as function of different sets of features used in the classification algorithm.
classification method are based on two completely separated datasets. Nevertheless, the combined use of optical and SAR data still confirms a clear improvement of the image classification accuracy for the Bam test case too.
C.	Adapazari case study
Based on the results concerning the Izmit and Bam case studies, an attempt to create a damage map has been performed. Unfortunately, the optical image acquired before the earthquake, was affected by cloud cover, hampering the exploitation of an optical change image to detect damage. The SAR intensity correlation has been used has a unique feature to attempt the damage map production. Three damage levels (low, medium and high) have been identified; each one corresponding to three thresholds defined on the intensity correlation values. The latter have been calculated within region extracted from a ground truth map provided by the Kandili Observatory and Earthquake Research Institute (KOERI) of Turkey. Figure 8 reports the damage map obtained for this case study and, to compare the results, the ground truth map. Despite the difference on the damage levels defined in the two maps (three for the satellite based map and seven for the ground truth one) the satellite damage map seems to fairly agree with the in situ survey. The SAR sensitivity to the damage caused by earthquake is confirmed again.

IV.	Conclusions
The capabilities and limitations of satellite remote sensing to detect damage due to earthquakes have been analysed in this work. We have also assessed different approaches for damage inventory and compared the relevance of different features which can be extracted from optical and radar data. Three case studies have been presented related to two destructive earthquakes. In the Izmit case the combination of SAR and IRS optical data allowed us to apply automatic procedures for damage classification. The capability to identify well defined damage classes, reaching the 90% of correct classification has been shown. In this favourable case, it has also been demonstrated the possibility to significantly improve the results taking advantage of the change detection potential of the InSAR complex coherence combined with optical data. The work has indicated the need of carefully selecting SAR image pairs with small spatial baseline to increase the usefulness of the coherence feature. The correlation with damage level observed in situ has been also demonstrated for the optical change image and SAR intensity correlation when data are aggregated within homogeneous regions (such as blocks of buildings). 
In the Bam case, even if the ASAR dataset had a very high spatial baseline, hampering the use of complex coherence, the sensitivity of SAR intensity correlation to damage level has been also demonstrated. Moreover, medium resolution optical

Figure 8. 	Comparison between the satellite based damage map (left panel) and ground survey damage map (right panel). Darker colours correspond to more damaged areas.

images revealed themselves moderately useful for our purpose. In fact, the combined use of SAR and optical data reached 77% of correct classification, confirming the complementarily of optical and microwave data. The apparently lower quality of the Bam result originates from the large spatial baseline of the SAR image pairs and the moderate resolution of the optical data. In the Adapazari case, the experience made in the previous cases has been used to test the capability to generate a product useful for rescue activities of civil protection. Despite the unavailability of useful optical images, due to cloud coverage, a damage level estimation has been performed by the SAR intensity correlation alone. Encouraging results have been obtained in this case too. The comparison with ground truth information confirms the sensitivity of SAR sensor to damage levels.
Acknowledgment
The work is partially funded by the EURORISK-PREVIEW Project.
References

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