CRHQ Mathieu Guérin

Highlanders of Central Vietnam and Cambodia: Economic and socio-cultural changes between 1975 and 2007 1

The Highlanders of Vietnam and Cambodia experienced dramatic changes in their livelihood during the last 30 years.They have been facing unprecedented migratory movements from the plains. The newcomers are farmers, civil servants, foresters, traders, coffee growers. They spread over their land, fundamentally shattering their way of life. This colonization process of the Highlands by outsiders took an appalling turn in Vietnam at the beginning of the 21 st century. Meanwhile, Cambodian highlands seem to follow the same path.

Forest clearing, intensive land development, land grabbing as well as pressures to change their habits affects the daily life of the native people of the Highlands. According to governments and international development agencies, these changes should allow a better integration of the Highlanders into their nation and the global world. However, many Highlanders suffer from the situation and want to stand for their land, their customs, and their identity.

The Highlanders of Vietnam and Cambodia, Jarai, Ede, Brao, Mnong, Stieng, and other indigenous people from the high plateaus at the border between the two countries have experienced dramatic changes in their livelihood over the last 30 years.

In February 2001 andin April 2004, the Highlanders of central Vietnam organized mass protests, standing for their land, their customs, their identity and religious freedom as Christians. The harsh response of the Vietnamese security forces drove thousands of people to flee to Cambodia. Some were granted a refugee status by UNHCR and were able to settle in a third country, mainly the United States of America, others were sent back to Vietnam. The 2001 and 2004 protest movement alerted the international community on the problems that the indigenous people of the Highlands were facing. These problems have their roots in the colonial era, when the French took control over the Highlands and integrated them into the neighbouring countries. However, recent socio-economic trends have, in many ways, worsened the situation of the native population. This talk explores the policies implemented in the Highlands of Vietnam and Cambodia in the last thirty years, including how policies affected the lives of Highlanders. It is not my purpose to pass judgment on the action of the Cambodian or Vietnamese governments, but to explain their actions, the origins of those actions, and the consequences for the Highlanders.

In April 1975, Saigon was conquered by the North Vietnamese army and the Vietcong. The Vietnam War was over and a new era began. The socialist regime of North Vietnam had succeeded in uniting the country. In December 1978, the Bo Doi invaded Cambodia, putting an end to one of the most notorious regime of the 20 th century, that of Pol Pot and the Khmers Rouge.

Between the two countries lies a space of Highlands, High plateau, dense forests, savannah, often referred to as the Central Highlands, that was neither Vietnamese nor Cambodian before the French took control over the Indochinese Peninsula. The Highlanders were not part of Cambodia or Vietnam. It's quite late in the colonial period that the French effectively controlled their territories, not before the 1930s for the core zone of the plateau.

Most of the Highlanders lived in villages that formed independent units, even if some, like the Jarai were loosely united under the spiritual leadership of the three Pötao. All the Highlanders had a strong connection with their natural environments, especially with the forests, and the streams and rivers. They practiced swidden cultivation, a form of shifting agriculture, usually within the territorial limits of the village, on land cleared by fire. Swidden provided rice, vegetables, corn, dyeing plants, textiles such as cotton or China grass. After two or three years, the swidden were left fallow between five to over twenty years, giving time for the vegetation and soils to regenerate. The Highlanders went to the forest to gather the wax of wild bees, tubers and non-timber forest product like rattan wood or wood oil. They also hunted small and large games for flesh. Some built boats, others captured and trained wild elephants. Their connection with the forest was also spiritual. The forest was deemed inhabited by spirits, the Yang, which the Highlanders respected and feared.

When the French came, they drew borders and incorporated the Western part of the Central Highlands to Cambodia and the Eastern part to Vietnam. The dwellers of the Highlands became Cambodian, Annamite and Cochinchinese, later Vietnamese. People from ethnic groups like the Jarai or the Mnong lived on the both side of the border. Families were split up. This situation was not really an issue as long as Indochina was united under French rule. It was then easy to cross borders. Things became gradually more complicated when independent Cambodia and Vietnam started to gain effective control over their margins and their borders.

After the end of the Vietnam War and the fall of the Khmer rouge regime, Vietnam and Cambodia had three priorities : to ensure their safety, to build up their nation and to develop their economy. The policies implemented to meet with these objectives had strong impact on the Highlands and the livelihood of their dwellers [START_REF] Guérin | Des montagnards aux minorités ethniques, quelle intégration nationale pour les habitants des hautes terres du Viêt Nam et du Cambodge ?[END_REF].

Between the late 1970s and the 1990s, the security forces of Vietnam and Cambodia succeeded in effectively controlling the Highlands. By 1977, the FULRO insurgency was totally defeated in Vietnam, while remnant of this forgotten army sought refuge in the province of Mondolkiri. The last FULRO guerillas fought the Vietnamese army in Cambodia with the Khmers Rouge. It was only in 1998 that the last strongholds of the Khmer Rouge in Mondolkiri and Ratanikiri agreed to stop opposing the Phnom Penh government.

Because it was a buffer zone, a border area, because of the fighting that took place over a long period of time in the Highlands, the High Plateau and mountainous parts of the Central Highlands have always been considered as a strategic zone by Vietnam and Cambodia. This explains why the military always had a strong presence in the Highlands. In Cambodia, security matters superseded all other issues in the Highlands until 1993-1994.

The situation was quite different in Vietnam. The FULRO guerrillas had not been a real threat to the Vietnamese government and army for a long time. Priority was given to the building of the nation and to the economic development of the Highlands, understanding that it would also serve national security.

In 1954 and then in 1975, the Vietnamese government found itself with the difficult task of trying to create a united nation in the framework of the socialist revolution, with a people from different ethnic groups. The official discourse presents Vietnam as a multicultural nation where every ethnic group is a flower that beautifies the country. Ho Chi Minh considered the Vietnamese nation as a family in which the compatriots of different ethnic groups would be the children and great-children of Vietnam, all brothers and sisters of the same flesh. It can be explained by the influence of the thought of Lenine and Staline who built up a multicultural Soviet Union.

If the official discourse insists on the strong links between lowlanders and highlanders, the Viet people tend to present themselves as superior to the Highlanders, but willing to help and support them. The Vietnamese ethnographers Dang Nghiem Van, Chu Thai Son and Luu Hung, in the 1993 edition of their book Ethnic Minorities in Vietnam, -tried to justify the ascendancy of the Viet over the Highlanders by explaining that the Viet and the other groups form a national community, the center of which was "naturally" in the plains mostly inhabited by the Viet, because the plains were more densely populated, wealthier, their technology more advanced, and the economy at an higher stage of development [START_REF] Dang | The Ethnic Minorities in Vietnam[END_REF].

Ethnic minorities are called compatriots, Dong Bao, which means they and the Viet people actually belong to the same country. The Vietnamese government launched huge ethnographic programs in order to record and study the oral literature and the culture of the ethnic groups of the Central Highlands. Following the 2001 riots, TV and radio stations broadcasted in local languages. The Vietnamese government did show a will to preserve the identity and culture of the people of the Highlands. Written scripts of local languages can be taught in schools. Meanwhile the school and health systems improved significantly in the Vietnamese Central Highlands. However illiteracy rates remain high among Highlanders and the recognition of their culture has always been limited to the "good" customs, as defined by the State and the Party. The recognition of the culture therefore remains only on a superficial level, such as music, dance or handicrafts.

The Vietnamese advisors who worked in Cambodia in the 1980s brought with them their vision of a multicultural nation. The 1981 and 1989 Cambodian constitutions, which had been written with the assistance of Vietnamese lawyers, included clauses to protect the cultural identity of ethnic minorities. The phrase chun cheat pheaktich, meaning ethnic minorities, appeared then in the political discourse to term the indigenous people. Afterwards, these provisions disappeared in the 1993 Cambodian constitution. There is indeed a huge contradiction within the Cambodian political view on the nation and the concept of ethnic minorities. The nation is traditionally seen as the nation of the Khmer. There is no distinction in everyday Khmer language between Khmer and Cambodian. Referring to the Cambodian as a whole the Cambodian, including ethnic minority members, will say "Khmer yung", "us the Khmer". This ethnic vision of the nation eventually led to a policy of assimilation since the independence of Cambodia.

But as a matter of fact, Cambodia remains a multicultural society that the Cambodian rulers have to deal with. In 1996, on the occasion of a national conference organised by the Centre for Advanced Study, a Cambodian research institute in social sciences linked to Berkeley University, the Cambodian "government was urged to give minorities a voice." [START_REF] Chaumeau | Govt urged to give minorities a voice[END_REF]) Ethnic minorities, including those from indigenous groups, the Chinese, the Vietnamese, the Cham, and other ethnic groups living in Cambodia aired their wish firstly to have the same rights than the Khmer and secondly to be able to follow their own customs.

Distinction between ethnic minorities living on the Cambodian territory for ages new comers have been emphasised at the time by officials. However, the Royal Government of Cambodia was never in a position to express a clear policy toward ethnic minorities. In fact, Cambodia is under tremendous pressure from the international community, that is to say foreign NGOs, UN agencies, bilateral and international cooperation agencies, embassies. The total external assistance to Cambodia represented 167% of the State revenue in 1998. Intense lobbying from the numerous foreign actors resulted in the State having to juggle with their wishes and its own aspirations. In order to please everybody, the political discourse is fluctuating between assimilation and ethnic minorities rights recognition. For example the term "indigenous" appears in the 2001 Land Law on the request on foreign lawyers while the concept is absent of the Cambodian legislation. Meanwhile, recent works by anthropologists, geographers or environmentalists who actually speak the languages of the Highlanders and spent time in the villages, like Ian Baird, Jeremy Ironside or Jonathan Padwe, show a strong will among the Highlanders of Cambodia to protect aspects of their life styles and their land rights, and also sometimes a high level of suspicion and skepticism toward development agencies and authorities.

In both countries a gap appeared between the official policy statements and State achievements. In Vietnam, although the political message is clear, it is often at variance with the facts. For instance, assimilation and acculturation policies might underlie projects designed for the development of ethnic minorities. Nevertheless, the specific identity of ethnic groups is seldom denied in the official discourse.

Paradoxically, the most damaging measures for the Highlanders have been those taken to develop the Highlands. The Vietnamese Central Highlands have been torn apart by the war.

Destructions have been extensive. Highlanders who had to leave their village because of the strategic hamlets policy during the war or to flee the fights were allowed to move back after 1975.

But, as soon as the war was over, the Vietnamese government also organized mass migrations of people from the northern part of the country to the Central Highlands. This migration scheme was part of the New Economic Zone programme that was responsible for the migration of 4 million persons, including 600,000 who settled in the Central Highlands.

For the Vietnamese rulers, the transfer of Viet farmers from areas of dense population into region of low population would result in a more rational distribution of population with respect to land at a national scale. Another consequence would be the development of the agriculture of the Highlands that would create new opportunities and improve the living standards of the dwellers, migrants and natives. Hamlets of migrants would also form a network of strategic residential areas that would increase the country's capacity for national defence. The migration of people from the plains was both an economic and a military project.

Three elements underlined the installation of the migrants in the Highlands:

-National enterprises were built from the plantations and factories left behind by the French. They introduced industrial agriculture in the Highlands.

-Cooperatives have been organized with migrants family.

-Besides, in order to make room for the new comers, an anti-swidden agriculture policy was implemented. Using exactly the same rhetoric than the French during the colonial era, swidden cultivation was presented as "primitive", "unproductive" and responsible for deforestation. A sedentarisation policy was implemented in the Highlands and the administration stimulated cash crops production.

As the historian Andrew Hardy explains it, "the idea was to create a modern, socialist socioeconomic system made up of enlightened migrants from the plains and reformed inhabitants of the uplands".

With the success of the cooperatives and national enterprises, the Central Highlands started to develop a reputation of a place of prosperity. Meanwhile the Doi Moi adopted in 1986 gave more freedom to the Vietnamese citizen to change their place of residence and to choose their activity. Free migrants, Viet but also members of the ethnic minorities from the northern part of the country, started to move to the Central Highlands. The coffee boom of the early 1990s created an incredible opportunity for those who were ready for this new adventure. Vietnam became the second largest coffee exporter in the World. Meanwhile the state farms and cooperatives were dismantled and shared between their workers. A frontier was open. Since then, hundreds of thousands of migrants left the lowlands and the northern region and settled in the Central Highlands (Hardy 2003).

Soon, the state found itself unable to control these free migrations, which had dramatic effects for the native Highlanders. They led to a complete demographic transformation of the Highlands. The population of the Central Highlands that was around 400,000 in 1926 jumped to 2.8 millions in 1991, and since the 1990s the population growth accelerated. The native highlanders became a minority not only in the Vietnamese nation but also on their own former territories.

The land that the migrants took to grow coffee and more recently cashew nuts was taken from the forest, from the fallow land of the swidden cultivators. Even fields of the natives have been bought or grabbed. Land grabbing is the major issue for native Highlanders. A few Highlanders took the opportunity offered by the coffee boom to improve their wealth but the vast majority lost a lot if not everything in the process. Besides, because of overproduction the price of coffee nuts fell in the late 1990s -before increasing again. Some farmers, migrants and natives, who had invested everything on coffee in the early 1990s bankrupted. The Highland farmers discovered that they were now not only dependent on climate and soils but also on the global market (De [START_REF] Koninck | Agriculture, environnement et sociétés sur les Hautes terres du Viêt Nam[END_REF][START_REF] Déry | La colonisation agricole au Viêt Nam[END_REF]).

In Cambodia, no mass migration to the uplands was organised by the government in the 1980s. It was only after 1994 and the restoration of peace that migrations to Ratanakiri and Mondolkiri really started, but not at the same scale than in Vietnam. We can estimate that approximately 26,000 lowlanders settled in the Highlands of Cambodia between 1979 and 2000, which falls far behind the millions of migrants who settled in the Central Highlands of Vietnam over the same period. However the same causes led to the same effects: deforestation, land grabbing, impoverishment of the natives. The Highlanders of Cambodia received the support of NGOs and had an access to the judicial system to protect their rights, even if the judicial system has often favoured the rich and the powerful people. They could utter their discontent. The Highlanders of Vietnam had no other choice but to protest in the streets in 2001 and 2004. The surveys conducted by Human Rights Watch and by independent researchers commissioned by the UNHCR show that they have been harshly punished by the security forces and the Vietnamese judicial system (Human Rights Watch 2002, HRW June 2006, Writenet June 2006).

It is however important to point out that the pressure on the Highlanders to change their lifestyle, to abandon shifting cultivation and their strong links to the forest, doesn't only come from the central governments of Vietnam and Cambodia. American Evangelical missions have dragged along hundred of thousands of people. Most of the programs implemented by the international community, UNDP, the NGOs, the Asian Development Bank, the European Union, France or USAID, aim at increasing the pace of economic development with little understanding of inhabitants of the Highlands or consideration for them. When Japan and ADB fund a bridge over the Mekong and a highway to Ratanakiri and Mondolkiri, they mostly help lowlanders and local or foreign companies who wish to settle upland. Lowlanders will buy or take land that formerly belonged to Highlanders. Companies, like the Chinese Pine tree company Wuzishan, are creating large plantations on land that was once used by villagers and that the central government gave away as long-term concessions.

The swallowing up of the Highlanders into the nation had been a painful and complex process in both countries. The Highlanders are facing unprecedented migratory movements from the plains. This colonization process of the Highlands by outsiders took an appalling turn in Vietnam. Meanwhile, Cambodian highlands seem to be following a similar path, but with their own special circumstances.

Major social and ecological strains, which eventually led part of the indigenous peoples to struggle, dominate the region at the turn of the 21 st Century. Vietnamese and Cambodia States are torn between different ends. National security, economic and social development of the Highlands, Nation erection and consolidation are partly antinomic to preservation of indigenous cultural identity and natural heritage protection. After the end of the wars security matters and economic development appeared as the main priorities.

In the process that brought together an economic and social development of the region and the cultural and material impoverishment of its indigenous dwellers, Vietnam is far ahead Cambodia. The Vietnamese Central Highlands are no longer the territory of the indigenous people who became a minority among the Highlanders. In fact, the problems of lowland demographic pressure and national security are much more acute in Vietnam than in Cambodia. This explains why migrations, which are the real propulsive power for the changes, reached such a high scale in Vietnam. Cambodia appears to be at a cross road between a quick economic development of the Highlands and a slower evolution. The former will result in despoliations for the Tampuon, the Brao, the Mnong, the Jarai and other indigenous groups.

The later would give time to allow the indigenous people to prepare themselves to face the outside world. The first would be to secure their right on the land they farm.

All over the globe, and especially in Southeast Asia, indigenous cultures are facing heavy pressure from the external world to change, and in many cases they themselves wanting change. The level of control of change is the key, and right now the Highlanders are rapidly losing control and influence to outsiders -lowland dominants groups, authorities and foreign institutions. Development schemes often have paternalist elements that tend to present indigenous peoples, not any more as savages, but as immature people who need to be educated for their own good. The concept of development has replaced the former "mission civilisatrice" of the colonial France in order to justify a new levelling evolution, which is part of globalisation. The end is to reach the standards, lifestyle, and socio-economic organisation of dominant western cultures. Much of foreign assistance to these countries, even if is not intended as such, is a tool to fulfil this transition. The acculturation policies that the indigenous people of the Indochinese Central Highlands are facing today fit in a much wider discourse in which Cambodia and Vietnam have been dragged into.

Paper presented at the conference Socio-culutral issues of Champa 175 years after its Disappearance, San Jose, July 7-8


Anne-Lise Chesnel
email: annelise.chesnel@gmail.com

Renaud Binet
email: renaud.binet@cea.fr

Lucien Wald
email: lucien.wald@ensmp.fr

DAMAGE ASSESSMENT ON BUILDINGS USING MULTISENSOR MULTIMODAL VERY HIGH RESOLUTION IMAGES AND ANCILLARY DATA

Keywords: Change detection, urban remote sensing, damage assessment, very high resolution images

Satellite images are useful to prevent major disasters and mitigate their impact on populations. Their analysis is usually manually conducted by operators. Automatic processing of very high resolution (VHR) images is critical when the images to analyse are acquired with different modalities: acquisition angles, spatial resolution, or even sensor; however, this situation is frequent in an operational scope. We propose a method to assess damage on buildings using a pair of VHR images and ancillary data. We assess its robustness against the different modalities. We show that the performance of our methodology decreases with the acquisition angles difference but is robust against changes in spatial resolution and against the use of images acquired with different sensors. Even in extreme conditions, damaged buildings are well detected. Our methodology leads to a global performance from 72 % with a difference angle of 80 o , to 93 % for a difference angle of 24 o .

INTRODUCTION

More and more major disasters are reported across the world because their aftermath in terms of human and economical losses are increasing. This increase is explained by the growing population and by its migration in areas that are prone to disasters like seacoasts. Remote sensing has proved its usefulness for the crisis mitigation through situation report and damage assessment, as acknowledged by the creation of the International Charter Space and Major Disaster [START_REF]International charter Space and Major Disasters[END_REF] or initiatives like UNOSAT [START_REF]United Nations Institute for Training and Research (UNITAR) Operational Satellite Applications Programme[END_REF]. In this operational scope, the required information is manually extracted from satellite images. Usually a reference image acquired before the disaster and a crisis image acquired after the disaster are compared to retrieve damage. Damage assessment on buildings is of interest because their collapses lead to most of the human losses. For this purpose, Very High Resolution (VHR) images are usually used because this observation scale allows a more reliable visual analysis. The production of information has to be as short as possible, hence the need of automation to speed it up. The images are to be registered, and this requirement is by fare acute when considering an automatic image analysis method.

On one hand, the crisis image has to be acquired as soon as possible following the disaster, regardless to the acquisition modalities; on the other hand, the reference image has to be as recent as possible, to avoid additional major changes that are not related to damage. Hence there is little chance for the crisis image to be acquired in the same conditions, or even with the same sensor, than the reference image.

Moreover, the multitemporal analysis of VHR images exhibits more natural changes that are not related to damage [START_REF] Adams | Application of high-resolution optical satellite imagery for post-earthquake damage assessment: The 2003 Boumerdes (Algeria) and Bam (Iran) earthquakes[END_REF][START_REF] Bitelli | Image change detection on urban area: the earthquake case[END_REF][START_REF] Chen | Urban damage estimation using statistical processing of satellite images: 2003 Bam, Iran earthquake[END_REF][START_REF] Sakamoto | Automatic detection of damaged area of Iran earthquake by high-resolution satellite imagery[END_REF]. This is for examples changes due to human activities, or illumination changes. Another major source of changes is the difference in acquisition angles. Theses natural changes have to be corrected or filtered out. Object-oriented methods allow to focus the analysis on the objects of interest, thus to partly avoid false alarms due to natural changes. We focus on the buildings, more precisely on their roofs because they are most often visible by means of remote sensing. For this purpose, we use ancillary data that consist of the buildings roofs outlines in the reference image only. This data can be obtained after a disaster while waiting for the crisis image, by segmentation of the reference image or from a Geographical Information System.

We first describe the images and the ancillary data used as inputs. Then the proposed method to register precisely the buildings roofs is described. The objects of interest being registered, the binary classification of the buildings to detect the damage is conducted and the conclusions are drawn.

INPUT DATA

The chosen area of study is Beirut, in Lebanon. It is particularly adapted to our study because several images are available, before, during and after the bombings in summer 2006. The images set is composed of panchromatic VHR images acquired with two different sensors: two reference images and three crisis images concerning the QuickBird sensor (0.6 m resolution); two reference images and two crisis images concerning the Ikonos sensor (1 m resolution). Considering monosensor and multisensor pairs, it represents 20 different "reference/crisis" pairs of images. The illumination parameters being different, some related shadows changes are observable in the images (figure 1). The images have also very different acquisition parameters: this difference is represented by the base to height ratio (B/H) associated to each images pair:

B/H = sin a 2 tan a 2 - sin a 1 tan a 1 2 + cos a 2 tan a 2 - cos a 1 tan a 1 2
, where (a 1 , e 1 ), (a 2 , e 2 ) are respectively the azimuth and the elevation of the satellite for the reference and the crisis images.

For our dataset, this B/H lies between 0.42 and 1.4, which is equivalent to a difference angle between 24 o and 80 o . The studied urban area includes high buildings, so these differences in acquisition generate large shifts of the roofs from one image to another. These shifts must be compensated to allow a later comparison of the state of the roofs. As often in a crisis context, we do not have any 3-D information about the buildings. We believe more realistic in an operational scope to have 2-D information about the buildings location, thanks to a Geographical Information System, a cadastre or through an image segmentation. Our methodology use as inputs a reference/crisis pair of images, and such a 2-D information as ancillary data.

These ancillary data are the outlines of the buildings roofs, that are manually extracted from only one master QuickBird reference image. Each image (reference and crisis) is rapidly registered to the master reference image according to the ground, using four Ground Control Points (GCPs). Our methodology is applied to the 20 different pairs of images.

The extracts of one of the reference/crisis pairs of images are presented in figure 1. The ancillary data is superimposed on the reference image; the corresponding outlines in terms of geographical coordinates are reported on the crisis image. We observe that a classical registration using GCPs is not enough to precisely register the images. A change detection method can not be applied directly in these conditions. We propose in the next section a method to register the roofs, and thus to allow a damage assessment on the buildings.

FINE BUILDINGS REGISTRATION

Using the ancillary data in agreement with one reference image, we propose a method that automatically searches for the buildings roofs outlines in the other images. This registration is individually conducted for each building, the observed offset being proportional to the B/H, but also to the building height. The registration method is based on correlation. Classically, such registration method uses a square window for the correlation computation; instead, we propose to use an adaptive window that fits the shape of each roof in the reference image. It aims to maximize the correlation coefficient between the pixels held in the building outline in the reference image and the pixels held in a translated building outline in the crisis image. The correspondence of the building outline is searched in a restricted anisotropic area: the main direction is defined by the epipolar direction, and the length in this direction depends on the B/H. A small offset in the orthogonal direction is allowed to compensate the ground registration errors. The method is described more precisely in [START_REF] Chesnel | Quantitative assessment of building damage in urban area using very high resolution images[END_REF].

The result of the building registration corresponding to the extracts of figure 1 is presented in figure 2. We observe that most of the building are correctly registered, despite the large roofs offsets (up to 100 pixels in the epipolar direction). The registration fails for a few buildings; several reasons can explain this. A large part of the errors are related to destroyed buildings. This is due to the loss of similarity between the state of the building in each image. However, registration for these collapsed buildings has little sense. The other errors are related to buildings occluded by other buildings. This is par-Fig. 2. Result of the fine building registration with the same pair of images. Most of the buildings are well registered; the errors are mainly destroyed and occluded buildings that can not be registered. ticularly likely when the acquisition incidence angle is high; indeed the images have incidence angle up to 45 o . In this case, the building can not be registered. There are some examples in the left part of the figure 2: three outlines do not seem to correspond to any building; the correct location is hidden by nearby higher buildings.

DAMAGE ASSESSMENT AND PERFORMANCE EVALUATION

The fine registration step allows a comparison of corresponding objects in the images. The evolution of the state of the buildings roofs can be quantified by a change detection method. As we are interested in assigning a damage grade to each building, the correlation coefficients obtained at the end of the registration step can be use to quantify the similarity of the roofs between the two dates. Other change features can be extracted: statistical, textural or morphological features for example. From this features set, a classification of each building is conducted to decide if it has been damaged or not. Because the features values depend on the images that are processed, a supervised classification is preferred. The drawback of this kind of classification method is that a training set has to be defined for each classification. However, it allows to fit the data with a high confidence level, and thus guaranties the robustness and the applicability of the method to other disasters cases. SVM are chosen for their capability to learn from a small training set, and their rapidity of execution. The classification uses only 10 patterns to classify the totality of the 430 buildings, five for each class. To assess the quality of the classification result, a real damaged grade is assigned to each building through a visual assessment. This real damage grade is compared to the decision of the classifier, and the rate of good classification for all the buildings is evaluated. In the next paragraphs, one investigates the impact of the different modalities of the image acquisition: difference in acquisition angles, spatial resolution and sensors.

Images acquisition angles influence

First, we applied our methodology to the six reference/crisis pairs of QuickBird images. The correlation coefficients obtained between the pixels held in the registered buildings outlines are computed. The classification results using SVM are shown in figure 3. The results obtained for the totality of the buildings are illustrated along with the specific classification performance for the intact buildings (green triangles-up) and for the damaged buildings (red triangles-down). Whatever the considered damage grade, the classification performance is increasing when the difference in images acquisition angles, represented by the B/H, decreases. Intact buildings are more affected by this phenomena. However, the method remains robust against extreme difference in acquisition angles. Considering a B/H equal to 1.4, equivalent to a difference angle of 80 o , a good classification performance of 72 % is reached; for a B/H equal to 0.42, equivalent to a 24 o angle difference, this performance increases to 93 %. The roofs registration is indeed more difficult when the B/H is high; however, registraerrors only explain a part of the classification errors. The main part of the errors is explained by the geometrical deformations of the buildings roofs when the satellite incidence angles is high. These deformations are due to parallax and make the similarity measure lower. Finally, as it was observed during the specific roofs registration step, occluded buildings are badly classified, because no information is available for them.

Images spatial resolution influence

We investigate the robustness of our method for two different spatial resolutions. For this purpose, we compare the results obtained on one hand from the QuickBird images pairs, and on the other hand from the Ikonos images pairs. The results are shown in figure 4, respectively with the losanges and the stars. One can see that the Ikonos images pairs lead to results that follow the same trend as with the QuickBird images pairs. We see the same linear decrease of performance along with the B/H. We conclude that our method gives equivalent results with different very high spatial resolutions. To confirm this conclusion, we have subsampled the QuickBird images to the resolution of the Ikonos one, while keeping the SNR constant. The damage assessment performances from these subsampled images are equal to the ones with the original images, confirming that the proposed method is robust against different spatial resolutions and sensor characteristics.

Multisensor images pairs

Finally, we evaluate the performance of our damage detection method when the reference image is acquired with a different sensor from the crisis image. As explained before, this is a common situation for analysts to use, in a disaster context, images acquired from different sensors. The good classification rates, presented in figure 4, show the robustness of our method considering multisensor images pairs (circles and crosses): the classification performance increases with decreasing value of B/H in the same way as the previous monosensor images pairs.

CONCLUSION

Damage assessment following a major disaster is often conducted using satellite images. This assessment must be conducted rapidly after the disaster, to help rescue teams and local government. However, the visual analysis of the images is a lengthy work, particularly when the resolution of the images increases.

We propose a damage assessment method based on a semi-automatic analysis of two VHR panchromatic images and some ancillary data that give the buildings outlines in a reference image only. We evaluate the performances of our method against different image acquisition modalities: angles of acquisition (incidence and azimuth angles), spatial resolution, and sensor. We show that the performances remain stable when the spatial resolution varies, and also when the images of the reference/crisis pair are acquired with two different sensors. We demonstrate the impact of a difference in the acquisition angles of the two images: the quality of the results decreases when the two images are acquired with increasing difference angles, but still gives satisfying results. These are promising results for a future operational application, where those difficult analysis conditions are often encountered.

Fig. 1 .

 1 Fig. 1. Extracts of a pair of QuickBird images illustrating the misregistration of the buildings when their height is not compensate during preprocessing. (top): Reference image with buildings outlines (input). (bottom): Crisis image registered to the reference image according to the ground; the buildings outlines from the reference image are reported using their geographical coordinates.

Fig. 3 .

 3 Fig. 3. Damage assessment performances based on Quick-Bird images pairs as a function of their B/H. 430 buildings are classified (black rhomb) among two classes: intact buildings (green triangles-up) and damaged buildings (red trianglesdown).

Fig. 4 .

 4 Fig. 4. Damage assessment performances for all images pairs as a function of their B/H. The monosensor QuickBird ages pairs (red losanges) are the results previously presented; the monosensor Ikonos images pairs are represented in purple stars; the multisensors images pairs are presented in blue circles for the Ikonos/QuickBird images and in green crosses for the QuickBird/Ikonos images.


Anne-Lise Chesnel
email: annelise.chesnel@gmail.com

Renaud Binet
email: renaud.binet@cea.fr

Lucien Wald
email: lucien.wald@ensmp.fr

Urban damage assessment using multimodal QuickBird images and ancillary data: the Bam and the Boumerdes earthquakes

Remote sensing has proved its usefulness for the crisis mitigation through situation report and damage assessment. Visual analysis of satellite images is conducted by analysts, however automatic or decision aid method are desired. We propose a semi-automatic damage assessment method based on a pair of very high spatial resolution (VHR) images and some ancillary data. It is applied to two disaster cases, for which the QuickBird images acquisition conditions differ. For each case, the two images also have very different viewing and illumination angles. Hence their comparison requires a preliminary registration; an automatic method adapted to VHR images is described. Then several change features are extracted from the buildings, and their relevance to assess damage on buildings is evaluated. Some textural features allow a damage assessment, but correlation coefficients are more efficient. Finally, a step toward the full automation of the method is done, skipping the supervision step of the classification process. We show the robustness of the global approach for both disaster cases with average performances closed to 75 % when 4 damage classes are discriminated, up to 90 % for a intact/damaged detection.

I. INTRODUCTION

Major disasters across the world are more and more reported because their impact in terms of human and economical losses is increasing. This increase is explained by the growing population and by its migration in areas that are prone to disasters like seacoasts. Remote sensing has proved its usefulness for the crisis mitigation through situation report and damage assessment, as acknowledged by creation of initiatives like the International Charter Space and Major Disaster [START_REF]International charter Space and Major Disasters[END_REF] or UN-OSAT [START_REF]United Nations Institute for Training and Research (UNI-TAR) Operational Satellite Applications Programme[END_REF]. In this operational scope, the required information is manually extracted from images acquired by satellites. Usually a reference image acquired before the disaster and a crisis image acquired after the disaster are visually compared to retrieve damage. Concerning damage assessment on buildings, Very High Resolution (VHR) images are usually used because they allow a more reliable visual analysis at this scale. The production of information has to be as short as possible, hence the need of automation to speed it up. Automatic methods for damage assessment are most of the time based on change detection methods. The most common applied method used to be pixel-based ones, like difference or ratio of images.

Recently, the improvement in spatial resolution of the remote sensing images leads to a widespread use of object oriented methods, e.g. post-classification comparisons. Some reviews of the principal methods can be found in [START_REF] Singh | Digital change detection techniques using remotely-sensed data[END_REF]- [START_REF] Lu | Change detection techniques[END_REF]. Whatever the change detection method that is used, the images are to be made comparable, i.e. registered. This requirement is by fare acute when considering an automatic image analysis method.

On one hand, the crisis image has to be acquired as soon as possible following the disaster, regardless to the sensor type and the acquisition parameters; on the other hand, the reference image has to be as recent as possible, to avoid additional major changes that are not related to damage. Hence there is little chance for the crisis image to be acquired in the same conditions (acquisition angles for example), or even with the same sensor, than the reference image.

Moreover, the multitemporal analysis of VHR images exhibits more natural changes that are not related to damage [START_REF] Adams | Application of high-resolution optical satellite imagery for postearthquake damage assessment: The 2003 Boumerdes (Algeria) and Bam (Iran) earthquakes[END_REF]- [START_REF] Sakamoto | Automatic detection of damaged area of Iran earthquake by high-resolution satellite imagery[END_REF]. This is for examples changes due to human activities, or shadow changes due to different illumination conditions. Another major source of changes is the difference in acquisition angles, the parallax effect leading to geometrical deformations. Theses natural changes have to be corrected or filtered out. All these differences represent a challenge for automatic images analysis methods. Object-oriented methods allow to focus the analysis on the objects of interest, thus to partly avoid false alarms due to natural changes. We focus on the buildings, more precisely on their roofs because they are most often visible by means of remote sensing. For this purpose, we use ancillary data that consist of the buildings roofs outlines in the reference image only. This data can be obtained after a disaster while waiting for the crisis image, by segmentation of the reference image or from a Geographical Information System.

We propose a method that allows a building damage assessment from a pair of images acquired in very different conditions (acquisition and illumination angles, season. . . ). It is applied to two earthquake cases: the data are described in part II. An object oriented registration method is explained in part III-A and applied to retrieve the location of the buildings roofs in the crisis image that corresponds to each one in the reference image. Then, as explained in part III-B, some change features are extracted from each building roof, to quantify the damage that may have occurred. The buildings are individually classified according to these features using a supervised classification tool. The figure 1 illustrates the main steps of the proposed method. The results are presented in part IV: the features that give the best performances are specified as a conclusion. Finally, the possibility to make our damage assessment approach automatic, skipping the supervised classification step, is considered in part V.

Allows partial discrimination of changes

Ground-truth construction

Objects of interest are registered

Quantitative assessment of the performance

Ancillary data

Fig. 1. Block diagram for damage assessment using a pair of panchromatic QuickBird images and ancillary data.

II. TEST-CASES AND DATABASES

The method is applied to two earthquake cases: the Bam (Iran) earthquake of December 26, 2003 and the Boumerdes (Algeria) earthquake of May 21, 2003. A pair of panchromatic QuickBird images is available for each disaster. The images have different illumination and acquisition parameters, that generate changes respectively in shadows and in image geometry. Three of the five damage grades of the European Macroseismic Scale (EMS) [START_REF] Grünthal | Cahiers du Centre Européen de Géodynamique et de Séismologie[END_REF] can be discriminated at most by a visual analysis, leading to four-classes classification: intact, EMS3, EMS4 and EMS5. As ancillary data, the outlines of the buildings roofs have been manually extracted from the reference image.

A. Bam earthquake data

The reference image has been acquired September 30, 2003; the crisis image dated January 3, 2004. Some seasonal changes can be observed (vegetation, river), as long as some changes in illumination conditions. 2168 buildings have been extracted and are divided as follows: 475 intact buildings, 201 buildings are associated to the damage grade EMS3, 407 to the grade EMS4 and 1085 to the EMS5.

B. Boumerdes earthquake data

The reference image dated April 22, 2002; the crisis image has been acquired the May 23, 2003, the day after tomorrow the earthquake. Helped with an analysis presented in [START_REF] Yamazaki | Damage detection from high-resolution satellite images for the 2003 Boumerdes, Algeria earthquake[END_REF], we have conducted a visual analysis of the images to extract buildings and to associate a damage grade to each one. 610 buildings are extracted: 452 correspond to intact buildings, 77 to the EMS3, 16 to the EMS4 and 65 to the EMS5.

III. METHODOLOGY

A. Roofs registration

Using the ancillary data in agreement with the reference image, we propose a method that automatically searches for the buildings roofs outlines in the crisis image. It allows to compensate for the parallax, that is not negligible for the subsequent change analysis method. The correspondence of each roof in the crisis image is individually searched in a restricted area that is defined by the acquisition angles of both images; the greater the base to height ratio of the images pair, the larger the search area. This registration step is based on the joint maximization of two complementary correlation coefficients computed between the homologous pixels inside the buildings roofs outlines. The first is computed on the images and the second is obtained from the same images filtered by the Canny edge detector. The latter is particularly adapted in an urban context, due to the presence of multiple objects with different radiometry (buildings, roads, trees. . . ). The former allow to discriminate for example edges due to shadows from building edges. Hence, the two correlation coefficients are complementary. For each location included in the search area, one compute a score that corresponds to the norm of the vector which coordinates are the two correlation coefficients. Its maximisation allows a fine subpixel registration of the building roofs.

The quality of the result of the fine registration step is a determining factor to the subsequent damage detection. An extract of the result is presented in figure 2: it shows the necessity to register specifically the buildings roofs (figure 2b) and the efficiency of the proposed registration method (figure 2c).

B. Damage assessment 1) Features extraction:

The method attributes change coefficients to each building by assessing the amount of change on their registered roofs. Two sets of object-oriented features are tested: one is based on correlation computed between the homologous pixels inside the buildings roofs, the other quantifies the change of texture of the buildings roofs.

The correlation coefficients used to quantify the damage correspond to the ones that have maximised the score during the registration step. These coefficients are sensitive to a nonlinear change in the image radiometry. More specifically, the correlation coefficient computed on the images filtered by a Canny edge detector is sensitive to the partial or complete collapse of a building that leads to a change in the roof morphology. A building collapse also leads to a disappearance of the building shadow, and so to weaker or even to a lack of roof edge.

To characterize the texture, a set of anisotropic Gabor filters is defined [START_REF] Coggins | A spatial filtering approach to texture analysis[END_REF], [START_REF] Porter | Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes[END_REF]. En illustration of this type of Gabor filter is given in figure 3 in the spatial domain and the corresponding filter in the frequency domain is given in figure 4. Their equation in the spatial domain is defined as followed:

f (c, l) = g(c, l) exp -2πjF c 2 + l 2 , ( 1
)
where g(c, l) = 1 2πσ 2 exp -

c 2 + l 2 2σ 2 ,
and F is the centre frequency. The scale factor σ is defined so that the high frequencies are more localised in space:

σ = µ/F, where µ is constant. ( 2
)
A set of 13 filters is defined, with respectively centre frequency: F = 0.1, 0.2, . . . , 1.2, 1.3.

The reference and crisis images are filtered and the amplitude of the results is retrieved. For each filter, the ratio of the energies computed for the pixels inside each roof outline before and after the disaster quantifies the amount of change between the two dates. 2) Building classification: From these features, the buildings are individually classified to quantify damage. A supervised classification based on SVM is chosen for its efficiency in terms of rapidity and performances. However, because the proposed method is to be used in an operational context, the required training set has to be as small as possible. SVM allows to reach good classification performance with a small training set composed of 5 samples per class.

To obtain a multiclasse classification, several binary classifications are made using a dichotomy method, through a decision tree. For example, an illustration in the case of a classification among four damage grades is proposed in figure 5: the decision is in this case the grade EMS4. Several different architecture of trees can be designed in the same manner.

IV. RESULTS

The effectiveness of the correlation coefficients and of the textural features is compared. A first classification is done using the 13 textural features; the dimension of the data being high and the size of the training being small, a second classification is made using only the two more discriminatory textural features (salient features). The results are gathered in table I.

We show that the textural features allow a discrimination of the four damage classes with a good classification from 59 % to 64 % of the buildings. Moreover, the correlation coefficients give better results, whatever the test-case: it improves the performances by about 15 %. Finally, the use of both features sets does not improve the results. We conclude that, although the textural features allow to a certain extent a damage assessment, it does not bring any additional information, comparing to the correlation coefficients alone.

Using only the correlation coefficients, we show that our method allows the identification of the 4 classes in the case of Bam with 78 % of good classification (figure 6); only three damage grades can be identified efficiently in the case of Boumerdes, the EMS3 being undetectable with our roof analysis approach (figure 7). Indeed, this damage grade corresponds in the case of Boumerdes to damage on the buildings walls; no change is observable on their roofs.

We assess the performance of the damage detection as a function of the number of distinguished damage levels. For both earthquake cases, the classification in three classes leads to an average performance equal to 75-80 %. Finally, when considering a two-classes problem, the performance increases up to 88 % in average. These results demonstrate that the proposed method is efficient and applicable to different damage types. Its performances are stable with different test-cases, except when damage are not structural and only affect the buildings walls. It explains the slight decrease of performances for the Boumerdes test-case. Moreover, the application of our method is fast and need a limited action of the image analyst.

V. GENERALIZATION CAPABILITY OF THE METHOD

Using a pair of VHR images and the ancillary data, the analysis of the images with the proposed method only requires an action of the operator during the classification step. A set of five buildings per class has been chosen. They are used to classify the whole buildings dataset. We investigate the possibility to use a classifier trained with one test-case, to classify the buildings of another test-case. The results are shown in table III.

Comparing these results to the ones obtain with a specific training for each test-case (table II), we show a slight and limited decrease of the performance when the classifier is

Fig. 2 .

 2 Fig. 2. Result of the registration step -case of Boumerdes. (a): reference image and ancillary data. -(b): crisis image registered to the reference image according to the ground, and buildings outlines reported using geographical coordinates. The buildings outlines are clearly shifted proportionally to the building height, preventing a comparison of the images. -(c): crisis image and automatically registered buildings outlines.

Fig. 3 .

 3 Fig. 3. Anisotrope Gabor filter in the spatial domain.

Fig. 4 .

 4 Fig. 4. Anisotrope Gabor filter in the Fourier domain.

Fig. 5 .

 5 Fig. 5. Example of a decision tree used to obtain multiclasse classification from the SVM.

Fig. 7 .

 7 Fig. 7. Confusion matrix obtained from the classification of the buildings of Boumerdes. Top: raw confusion matrix -Bottom: confusion matrix normalised per damage grade.

TABLE I PERFORMANCES

 I OF THE DAMAGE ASSESSMENT AS A FUNCTION OF THE USED FEATURES.

		Test-case
		Bam	Boumerds
	13 textural features	64 %	60 %
	Salient textural features	64 %	59 %
	13 textural features +	71 %	64 %
	2 correlation coeff.
	Salient textural features +	76 %	64 %
	2 correlation coeff.
	2 correlation coeff. alone	78 %	74 %

% 25 % 14 % 3 % 6 % 6 % 0 % 5 % 14 % 44 % 1 % 7 % 14 % 25 % 85 %

  Fig. 6. Confusion matrix obtained from the classification of the buildings of Bam. Top: raw confusion matrix -Bottom: confusion matrix normalised per damage grade.

					Real damage grade
				Intact EMS3 EMS4 EMS5
			Intact	347	40	13	0
	Estimated EMS	87	95	71	7
	damage	EMS4	38	62	220	50
	grade		EMS5	3	4	103	1028
			Total	475	201	407	1085
	Global performance: 78 %
	Kappa Coefficient: 0.66
				REAL DAMAGE GRADE
			Intact	EMS3	EMS4	EMS5
	Intact	73 % 20 % 3 % 0 %	100 %
	ESTIMATED DAMAGE GRADE	EMS4 EMS3	18 % 47 % 18 % 1 % 8 % 31 % 54 % 4 %	0 % 20 % 40 % 60 % 80 %
		EMS5	1 % 2 % 25 % 95 %
					Real damage grade
				Intact EMS3 EMS4 EMS5
			Intact	383	51	4	9
	Estimated EMS3	15	4	1	0
	damage	EMS4	23	11	7	1
	grade		EMS5	31	11	4	55
			Total	452	77	16	65
	Global performance: 74 %
	Kappa Coefficient: 0.39
		Intact	Intact 85 % 66 REAL DAMAGE GRADE EMS3 EMS4	EMS5	100 %
	ESTIMATED DAMAGE GRADE	EMS4 EMS3					0 % 20 % 40 % 60 % 80 %
		EMS5

trained with another test-case when considering a four-classes classification. The classification among three classes gives almost equivalent results. Finally, the results of the damage detection (classification intact/damaged) are strictly equivalent in both cases. We show that the learning step could be skipped if necessary, making the method fully automatic. Moreover, if the number of damage grades to be distinguished is lower, the precision of the results would be exactly the same. These are promising results for a future operational application.

VI. CONCLUSION

We describe a building damage assessment method based on a pair of very high spatial resolution images and some ancillary data that contain the buildings footprints in the reference images. It has been applied to two disaster cases, in conditions that are close to an operational application. The images are acquired in realistic conditions (most recent reference image, first crisis image): they have different acquisition angle and exhibit changes in shadows and vegetation due to the difference in season. Moreover, the method is fast and closed to be fully automatic. It gives satisfactory results, particularly when the aim is to detect damaged buildings among intact ones.

To make our method fully automatic, we are currently working on the registration between real map/cadastre and a VHR image acquired with a high off-nadir angle. Future improvement would involve the extraction of buildings outlines directly from VHR images. Morphology based approaches seem promising and will be investigated.


A.-L Chesnel
email: anne-lise.chesnel@cea.fr

R Binet

L Wald

Damage assessment on buildings using very high resolution multimodal images and GIS

Satellite images are more and more used to prevent natural disasters and mitigate their impact on populations. Their analysis is usually manually conducted by operators. Automatic processing of very high resolution (VHR) images is critical when the images to analyse are acquired with very different acquisition angles; however, this situation is frequent in an operational scope of a natural disaster. We propose a method to assess damage on buildings using a pair of VHR images whose dates of acquisition encompass the disaster, and a geographical information system that contains the buildings footprints. It takes into account the acquisition parameters of the images. We assess its robustness as a function of the difference between the acquisition angles of the images. For this purpose, we adopt the framework of an operational scope and use six different VHR images that have been acquired successively before and during the period of interest. We show that the rate of false-alarms increases with this difference of acquisition angles. However, even in extreme conditions, damaged buildings are well detected. Our methodology leads to a global performance of the damage detection from 69 % with a difference angle of 80 o , to 90 % for a difference angle around 24 o .

Introduction

Natural disasters have an increasing impact in terms of economical and human losses. This is due to the increasing population and to its migration in areas that are prone to disasters like seacoasts. Local authorities of the affected area need to know rapidly the disaster extent [START_REF] Allenbach | Rapid eo disaster mapping service: Added value, feedback and perspectives after 4 years of charter actions[END_REF] to limit its impact on population and material. For this purpose, remote sensing is useful, as acknowledged by the creation of the International Charter Space and Major Disasters (International-Charter, 2007) by 2000, and by initiative like Unosat (UNOSAT, 2007). In this operational scope, the required information is generally manually extracted from the images acquired by satellites: an archive image -the reference image -is compared to an image acquired after the disaster -the crisis image. The processing time has to be as short as possible, hence the need of an automatization to speed it up. Automated methods applied to medium to high resolution images have been proposed, but their performances are hardly addressed, no ground-truth being available. Very High Resolution (VHR) images allow such an assessment. However, the multitemporal analysis of these VHR images exhibits more natural changes that are not related to damage [START_REF] Adams | Application of high-resolution optical satellite imagery for post-earthquake damage assessment: The 2003 boumerdes (algeria) and bam (iran) earthquakes. Research Progress and Accomplishments[END_REF][START_REF] Bitelli | Image change detection on urban area: the earthquake case[END_REF][START_REF] Chen | Urban damage estimation using statistical processing of images: 2003 bam, iran earthquake[END_REF][START_REF] Sakamoto | Automatic detection of damaged area of iran earthquake by high-resolution satellite imagery[END_REF], like shadow changes, vegetation modifications and other changes due to human activities. Another major difficulty is the registration of the VHR images. When they are not acquired with the same acquisition angles, difficulties are encountered notably for objects higher than bare-ground. All these changes have to be corrected or filtered out.

We adopt an object-oriented approach. We focus on the buildings, more precisely on their roofs because they are most often visible by means of remote sensing. It avoids false alarms due to natural changes that occur around the buildings, on the roads for example. For that purpose, we use a Geographical Information System (GIS) built from an archive image of the studied area.

We propose in this work to use a method of damage assessment that includes a preliminary subpixel registration of the buildings roofs. It takes into account the acquisition angles of the images. We applied this method on the case of study of Beirut, in Lebanon. Some buildings in the area of Haret Hreik have been destroyed in summer 2006, following bombings. This event is particularly of interest for our study because several QuickBird images, acquired with very different acquisition angles, are available: two reference images and three crisis images. It allows to investigate the The data used in this study are presented in a first step. The registration method is explained in a second part. The method to detect the damage is presented in a third part. Finally, the results are analysed and conclusions are drawn.

Data used

The used in this study are panchromatic QuickBird images, acquired before and after the bombings of Beirut, that occurred from 12th of July 2006 to 14th of August 2006. We studied more specifically the area of Haret Hreik, where extensive destructions are observable. Two reference images acquired by QuickBird without clouds have been found. Then, three successive crisis images are available, the last acquired at the end of the conflict (table 1). From these images, different pairs of images reference/crisis can be used to detect damage. To quantify the difference of acquisition parameters for each pair, the base to height ratio (B/H) is computed (table 2). The larger the B/H ratio, the more different the acquisition angles.

First, we note that none of the available images have the same acquisition parameters (table 1). This is confirmed by the table 2: the B/H ratio varies from 0.42 to 1.40; it represents absolute difference angles from 24.3 o to 80.1 o . Note also that the two first crisis images (22 July 2006 and9 August 2006) present very low elevation angles. This is often the case for the very first images acquired after a disaster, because they are acquired as soon as possible, without waiting for ideal conditions. Figure 2 illustrates the problem of registration with the reference image of 19 June 2006 (a), and the crisis images of 9 August 2006 (b) and 12 August 2006 (c). The three images are registered according to the ground, as confirmed by the red mark on the left: it is at the same location in the three images. A high building in the centre is highlighted: the higher the B/H ratio, the larger the shift between the two images. We also see that a low building (on the right of the images) can be hidden by a higher one (figure 2b), preventing its analysis.

We also use in this study a Geographical Information System (GIS) as input data. It contains the footprints of the building roofs, in agreement with the reference image. This GIS has been built manually; however, the footprints of the building roofs could have been retrieved by other means: a Digital Elevation Model with fine spatial resolution, or an segmentation and classification of the reference image.

The extraction of the building roofs is limited to the reference image; then the same GIS is used with the three pairs of images that correspond to the three crisis images. In this way, a monitoring of a site of interest at successive dates is possible. By comparison with the crisis images, we have identified the buildings of the area that have been destroyed. It will represent a ground-truth to which we will compare the result of our damage detection method. The GIS, overlaid on the reference

Registration

To compare the images in an automatic way, they have to be precisely registered. Registration methods found in the literature [START_REF] Brown | A survey of image registration techniques[END_REF][START_REF] Zitova | Image registration methods: a survey[END_REF] are not adapted to VHR images with different acquisition angles. Most of them aim to build a model of the transformation from the master image to the slave image; deformations in VHR images are difficult to model, because of the discontinuity of the surface elevation due to the presence of buildings. Thus we propose to register the images in two steps. First we register the images according to the ground (image registration). As seen in figure 2, the top of the buildings are still not registered: a method of subpixel registration of the building roofs is then applied, using the GIS (roof registration). The GIS is already registered to the reference image.

Image registration

The original images are ortho ready standard images. We assigned to the reference image of the 16 June 2006 the role of master image, and registered all the others to this image. We orthorectified the images using the Rational Polynomial Coefficients provided with the data and the SRTM Digital Terrain Model. We registered the images to the master image with a set of four Ground Control Points.

Roof registration In addition to the registration of the images, we propose a method to register each building roof precisely. Our registration method estimates the maximum a posteriori of the correlation computed on the group of pixels held in the roof footprints in both images. The footprint defined in the reference image by the GIS is translated in the crisis image by a quantity o estimated by the algorithm of roofs registration (equation 1). Let R 1 and R 2 be respectively the reference and the crisis images with zero mean. Let c and l be respectively the column and the line coordinates, and k c and k l be respectively the shift value of the roof footprint in the crisis image along the columns and along the lines.

o = arg max kc,k l            c,l R 1 (c, l).R 2 (c + k c , l + k l ).H(c, l) c,l R 2 1 (c, l).H(c, l). c,l R 2 2 (c + k c , l + k l ).H(c, l)            (1)
where

H(c, l) = 1 if R 1 (c, l) ∈ roof footprint 0 otherwise
The function H(c, l) may be defined by the means of a segmentation or an extraction of buildings footprint. In our case, its values are assigned by the means of the GIS. The position (k * c , k * l ) of the maximum correlation yields the offset o.

The offset o can be written as the sum of a ground offset o g due to the ground misregistration and a height offset o h due to the unknown building height: o = o g + o h . Let o ⊥ be such that o g ∧ o ⊥ = 0. The ground offset o g has no expectable orientation. On the contrary, the height offset o h is solely dependent on the viewing angles for both images and on the building height. For intact buildings, this offset is observed in the epipolar direction.

Equation ( 1) is applied to a limited search area. The height offset o h is due to the height of the constructions, so its amplitude differs for each building. On the contrary, we will suppose that the ground offset does not vary much. These differences are exploited to define an unique search area. Note that a 2D search is necessary due to the unpredictability of the ground offset, reducing the relevance of an epipolar image projection. The search interval is set along the o ⊥ axis as a function of the expected registration error of the ground between the images, and along the o h axis as a function of the estimated maximum height of all the buildings.

Damage detection

The correlation coefficient that corresponds to the best offset (k * c , k * l ) for each building yields information about the similarity quality of the roof between the two considered images. We use this coefficient associated to each building to decide whether it is damaged or not. The correlation coefficient spans between -1 and +1. Different tests have shown that depending on the test case (different areas, damage types, building types, atmospheric conditions. . . ), the optimal threshold for the correlation coefficient between intact and damage building differs. So a threshold adapted to a given disaster has to be decided. For this purpose, we chose a supervised classifier based on Support Vector Machines (SVM) [START_REF] Vapnik | The nature of statistical learning theory[END_REF][START_REF] Burges | A tutorial on support vector machines for pattern recognition[END_REF]. To meet the requirements of an operational scope, the required training set is constrained to be small, with five examples of intact buildings and five other examples of damaged ones.

Results and discussion

The methodology is applied to each of the six images pairs. The computation for the registration and the damage detection is fast (a few minutes). The 10 training samples (5 per class) for the classification with SVM are randomly chosen among the buildings of the GIS, according to the class they belong. The performances are evaluated on the whole dataset.

The classification performances are given as a function of the B/H ratio in figure 3. The rates of good classification for the intact buildings and for the damaged buildings appear separately (respectively in green and in red), along with the mean performance (in black). There is a clear trend of performance decrease when the B/H ratio increases (figure 3). Considering the intact and the damaged class separately, we observe that the damaged class is detected with a better accuracy than the intact one. Moreover, its associated performance is less affected by a high B/H ratio. Few damaged buildings are not detected. The main origin of the classification errors is false-alarms, i.e. the intact buildings that are classified as destroyed. A precise analysis shows that a large part of these errors are due to registration errors, for example buildings that are hidden by another one or located in its shadow. Figure 4 illustrates one of this situation. No information can be retrieve from these occluded buildings. We also point out that a lower satellite elevation leads to a coarser spatial resolution; a large difference of spatial resolution between the reference and the crisis image could also explain a decrease of similarity, even for intact buildings.

Conclusion

Following a disaster, it is most of the time not possible to chose the acquisition angles of the very first crisis images available. However, the analysis of crisis images is often conducted in comparison to a recent ante-disaster image. Thus one cannot make sure that both images will have the same acquisition parameters. One needs to use methods of analysis adapted to such situations. We propose a method to detect damage on buildings that takes into account this consideration. We evaluate in this paper the robustness of this method, as a function of the difference in acquisition angles. For that purpose, we use six different pairs of VHR images, and evaluate the quality of the damage detection.

First, we show that our method is efficient to detect damage for a moderate B/H ratio. Our method is specifically more efficient for damaged buildings (from 77 % to 97 % of correct classification), even in extreme situation where difference between the acquisition angles represents 80 o . The difficulty is to limit the false-alarms rate. We observe that this rate increases with the difference between the acquisition angles. A precise analysis shows that the difficulty is due to registration errors. Particularly, occluded buildings represent a large part of these errors.

More work on this topic is required to improve the results; several possibilities could be considered. One of these is to detect such occluded buildings, and to base the classification decision on the state of the buildings in its neighbourhood. It could be adapted to the case of a widespread disaster, like a hurricane, an explosion or a tsunami.

  Figure 1: In red, three marks are drawn in (a): on the ground (left), on the roof of a high building (centre) and on the roof of a low building (right). They are reported in the crisis images (b and c) using the geographical coordinates. The mark on the ground (left) is correctly registered for both crisis images, whereas the buildings roofs are shifted. -Note in (b) that the low building on the right is hidden by another one.

Figure 2 :

 2 Figure 2: Area of Haret Hreik, in Beirut, Lebanon. Image acquired in 19 June 2006 (reference image). The footprints of the 430 buildings contained in the GIS, used as additional input, is overlaid. The green corresponds to intact or not detectable damage (325 buildings); the yellow to buildings destroyed between the 19 June 2006 and the 22 July 2006 (74 buildings); the red to buildings destroyed between the 22 July 2006 and the 9 August 2006 (21 buildings); the blue is for buildings destroyed between the 9 August and the 12 August 2006 (10 buildings).

Figure 3 :

 3 Figure3: Rate of good classification for the buildings as a function of the B/H ratio. Lines result of a linear regression for intact buildings (green), destroyed buildings (red) and whole dataset (black).

Figure 4 :

 4 Figure 4: Classification result -Extracts of the 19 June 2006 reference image (a) and the 22 July 2006 crisis image (b). Green corresponds to intact buildings, red corresponds to destroyed ones; Solid fill means correct classification, contrary to the footprint with no fill. The building in the center is intact but is classified as destroyed because it is hidden by another building: an important change is detected.

Table 1 :

 1 Acquisition angles of the QuickBird images used as input in this study.

		Date	Sat. elevation angle	Sat. azimuth
				above the horizon	(clockwise due North)
	12 July 2005		66.0 o	58.0 o
	19 June 2006		84.6 o	244.0 o
	22 July 2006		45.8 o	279.2 o
	9 August 2006		44.6 o	274.4 o
	12 August 2006		63.6 o	208.0 o
	Table 2: Base to height ratio (B/H) for the six different images pairs used as input.
					Crisis images
				22 July 2006 9 August 2006 12 August 2006
	Reference 12 July 2005	1.34	1.40	0.91
	images	19 June 2006	0.90	0.93	0.42
	robustness of our method in a dense urban area with high buildings, as a function of the acquisition
	conditions.