VERY HIGH RESOLUTION LAND COVER EXTRACTION IN URBAN AREAS : VERY HIGH RESOLUTION URBAN LAND COVER EXTRACTION USING AIRBORNE HYPERSPECTRAL IMAGES

During last decade, needs for high resolution land cover data have been growing. Such knowledge is namely often required in environment monitoring studies. Thus, to answer these needs, national mapping or environment agencies, in many countries, have undertaken the production of such large scale national land cover database. Nevertheless, these databases provide a general classification and may not suit some specific (often new) applications requiring a semantic or geometric finer level of details. That is to say that, on one hand, additional land cover classes should sometimes be specified, whereas, on the other hand, some existing classes should be delineated at a finer level. More particularly, in urban areas, knowledge concerning very high resolution land cover and especially material classification are necessary for several city modelling applications. Most of these applications are still experimental scientific ones in various fields such as micro-meteorology, hydrology, pollutants flow monitoring and ground perviousness monitoring. Thus, knowledge concerning the roofing materials or the different kinds of ground areas (pervious, vegetated, impervious...) are required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale since no existing map contains such information. However, remote sensing imagery of urban environments from airborne acquisitions namely still remains a major scientific issue, since on one hand, urban areas are characterized by a high variety of materials, and on the other hand, results provided by most of the traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral imagery. Thus, the present experiments are part of a work aiming at designing a future superspectral camera system dedicated to high resolution urban land cover classification applications, and especially material mapping. The choice of optimal band sets will here be processed from a set of airborne hyperspectral data. A data acquisition campaign named UMBRA has recently been carried out thanks to the French collaboration of IGN and ONERA. Data have been captured over two French cities chosen for their difference in building architecture, urbanization planning and their variety in urban material. Airborne images have been acquired simultaneously by multispectral and hyperspectral cameras with a ground sampling distance ranging from 0.12m for multispectral to 1.6m for hyperspectral in the SWIR channels. The images were radiometrically and geometrically calibrated and have a noticeable low signal-to-noise ratio. The first urban land cover / material classification results obtained from this new reference data set will be presented in this paper. 1 IGN is the French National Institute of Geographic and Forest Information 2 ONERA is the French Aerospace Lab, that is to say the French aeronautics, space and defense research lab 1. NEEDS AND POTENTIAL APPLICATIONS INVOLVING VERY HIGH RESOLUTION URBAN LAND COVER In urban areas, knowledge about very high resolution land cover and especially maps of the urban materials are required by several city modelling applications. Urban environment is indeed strongly influenced, in terms of ecology, energy and climate by the present materials. These materials can be either natural or artificial. Most of these applications are still experimental scientific ones such as micro-meteorology, hydrology, pollutants flow monitoring and ground perviousness monitoring. Several possible applications requiring very high resolution knowledge about urban land cover and materials are listed in (Heldens et al., 2011) and (Shafri et al., 2012). 1.1 Quantification of pollutant flows from roofs in urban rainwaters Some roofing materials can generate pollutant elements. Thus, in the actual context of the EuropeanWater Framework Directive (2000/60 CE), whose aim is to obtain a good ecological state of aquatic environments, it seems necessary to reduce the production of pollutants at their sources. This implies to identify sources and to quantify emissions. Several kinds of pollutions are generated by roofing materials. First, it has been proven that roof runoff water plays an important role in the high metallic concentration levels in urban rainwater since metallic elements are generated by corrosion of roof materials before being swept away by rainwater. Zinc-based materials are largely used in urban areas, especially for infrastructure, such as furniture or siding and roofing for buildings. Exposed to atmospheric conditions, these materials are progressively corroded. During a rain event, a part of the corrosion products formed at their surface will be released and washed off. In Paris, experiments have established that atmospheric corrosion of roofing materials could be a major source of zinc, cadmium, lead and copper during wet weather (Chebbo et al., 2001). Several researches on identification of metals from roofing materials have been carried out, showing that zinc emissions are mainly in the labile form (Heijerick et al., 2002), which is bioavailable and therefore harmful to aquatic organisms (both animals and plants). Copper roofs have also been identified as a possible source of pollution. Last, some other kinds of roofing materials can help to release organic polluting elements (polycyclic aromatic compounds, organic carbon) due to a not visible bitumen layer (Lemp et al., 2004 ; Lemp et al., 2005). Laboratory experiments have often already been done to model pollutant runoff rates for roofing materials (for instance, see (RobertSainte, 2009) for metallic elements). Knowledge about the different roof coverage areas is thus required to be able to extrapolate these results to whole drainage areas: a map of roofing materials is thus needed. Previous works aiming at extracting maps of roofing material out of airborne imagery exist. (Le Bris et al., 2009) performed supervised classification of red-green-blue-near infra-red aerial ortho-images. Non roof areas were masked using building objects of a topographic database. (Lemp et al., 2004 ; Lemp et al., 2005) used hyperspectral HyMap data in association with Lidar measurements. Slope information derived from such 3D data was shown to be useful to help the discrimination between some roofing material classes. More recently, (Chisense et al., 2012) obtained good results using HyMap data: 11 features were first extracted (using projection pursuit and LDA) and supervised classification was then performed. 1.2 Monitoring of asbestos-cement roofs Another possible application in the field of urban materials concerns the monitoring of asbestos-cement roofing materials (Heldens et al., 2011 ; Bassani et al., 2007 ; Marino et al., 2000). Asbestos-cement based materials can indeed be dangerous for human health, especially when they are deteriorated. Therefore, it is important to be at least able to evaluate the amount of buildings covered by asbestos-cement roofing sheets. Evaluating their deterioration status is also a useful issue. A method to achieve this using hyperspectral images has been proposed in (Bassani et al., 2007): it focuses on special spectral bands identified from spectrum analysis. 1.3 Electro-magnetic wave propagation models used to define the best location for telecommunication infrastructure Such possible application is mentioned by (Carrileiro et al,. 2001). (Carrileiro et al,. 2001) indeed aimed at obtaining material maps in order to enrich 3D building models used as input data of electro-magnetic waves propagation simulators used to define the best location for telecommunication infrastructures (that is to say antennas). 1.4 Determination of road type and monitoring of road condition Other applications in the field of urban materials concern road materials. At least, maps of road types (cobblestone, asphalt ...) can be useful for some applications. A more important and complex application focuses on the monitoring of road condition: such information indeed offers great interest for authorities in charge of the planning of road network renovation projects. Extracting this knowledge out of aerial data could be a way to avoid expensive and long field investigation (Herold et al., 2004b). Two examples of methods aiming at determining road condition from aerial hyperspectral data are presented in (Herold et al., 2004b) and (Mohammadi, 2012). These works focus on special spectral bands identified from spectrum analysis. 1.5 Monitoring of ground perviousness Two kinds of applications requiring knowledge about ground perviousness exist. On one hand, it has been shown that the continuous development of impervious areas (especially in the periphery of cities), such as wide parking areas in commercial districts, plays an important role in the aggravation of flooding events, both in terms of magnitude and speed. Thus, having tools making it possible to monitor the extension of impervious areas and to check their appliance to new legislations would really be useful. On the other hand, perviousness maps are required by (“micro”) hydrological models (Heldens, 2011). For instance, (Kermadi et al., 2010) extracted a land cover classification out of multispectral images with a very high spatial resolution such as BDOrtho or QuickBird and then integrated this data in hydrological models. This example is not a “micro” hydrological one and land cover classes do not correspond to specific materials. Other studies aiming at mapping ground perviousness in urban areas used unmixing approaches applied to lower spatial resolution hyperspectral data (Roessner et al., 2001; Demarchi et al., 2012). 1.6 Weather models Very fine knowledge concerning urban land cover (in terms of materials, perviousness and vegetation) a