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Stilla, Uwe

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009
Daniel Frey and Matthias Butenuth
Remote Sensing Technology
Technische Universität München
Arcisstr. 21, 80333 München, Germany
daniel.frey@bv.tum.de, matthias.butenuth@bv.tum.de
KEY WORDS: System, Classification, Statistics, Multisensor, Integration, GIS, Disaster
In this paper, a near-realtime system for classification of GIS-objects is presented using multi-sensorial imagery. The system provides
a framework for the integration of different kinds of imagery as well as any available data sources and spatial knowledge, which
contributes information for the classification. The goal of the system is the assessment of infrastructure GIS-objects concerning their
functionality. It enables the classification of infrastructure into different states as destroyed or intact after disasters such as floodings
or earthquakes. The automatic approach generates an up-to-date map in order to support first aid in crisis scenarios. Probabilities are
derived from the different input data using methods such as multispectral classification and fuzzy membership functions. The main core
of the system is the combination of the probabilities to classify the individual GIS-object. The system can be run in a fully automatic
or semi-automatic mode, where a human operator can edit intermediate results to ensure the required quality of the final results. In this
paper, the performance of the system is demonstrated assessing road objects concerning their trafficability after flooding. By means of
two test scenarios the efficiency and reliability of the system is shown. Concluding remarks are given at the end to point out further
A significant increase of natural disasters such as floodings and
earthquakes has been observed over the past decades (Kundzewicz
et al., 2005). There is no doubt that the disasters’ impact on the
population has dramatically increased due to the growth of pop
ulation and material assets. The regrettable death of people is
accompanied by heavy economic damage, which leads to a long
term backslide of the regions hit by the disaster. This situation
calls for the development of integrated strategies for prepared
ness and prevention of hazards, fast reaction in case of disasters,
as well as damage documentation, planning and rebuilding of in
frastructure after disasters. It is widely accepted in the scientific
community that remote sensing can contribute significantly to all
these components in different ways, in particular, due to the large
coverage of remotely sensed imagery and its global availability.
However, time is the overall dominating factor once a disaster
hits a particular region to support the fast reaction. This becomes
manifest in several aspects: firstly, available satellites have to be
selected and commanded immediately. Secondly, the acquired
raw data has to be processed with specific signal processing algo
rithms to generate images suitable for interpretation, particularly
for Synthetic Aperture Radar (SAR) images. Thirdly, the inter
pretation of multi-sensorial images, extraction of geometrically
precise and semantically correct information as well as the pro
duction of (digital) maps need to be conducted in shortest time-
frames to support crises management groups. While the first two
aspects are strongly related to the optimization of communication
processes and hardware capabilities, at least to a large extend, fur
ther research is needed concerning the third aspect: the fast, inte
grated, and geometrically and semantically correct interpretation
of multi-sensorial images.
Remote sensing data was already used in order to monitor natural
disasters in the year 1969 (Milfred et al., 1969). Particularly, in
the case of flooding a lot of studies are carried out to infer in
formation as flood masks from remote sensing data (Sanyal and
Lu, 2004). The flooded areas can be derived from optical im
ages (Van der Sande et al., 2003) as well as from radar images
(Martinis et al., 2009) via classification approaches. Zwenzner
(Zwenzner and Vogt, 2008) estimates further flood parameter as
water depth using flood masks and a very high resolution digital
elevation model. Combining this results with GIS data leads to
an additional benefit of information and simplifies the decision
making (Brivio et al., 2002, Townsend and Walsh, 1998). The
combination of the GIS and remote sensing data is often carried
out by overlaying the different data sources. But, there are only
few approaches which use the raster data from imagery to assess
the given GIS data. In (Gerke et al., 2004, Gerke and Heipke,
2008) an approach for automatic quality assessment of existing
geospatial linear objects is presented. The objects are assessed
using automatically extracted roads from the images (Wiedemann
and Ebner, 2000, Hinz and Wiedemann, 2004). However, in case
of natural disasters the original roads are destroyed or occluded
and, therefore, it is not possible to extract them using the original
methods. Hence, new approaches have to be developed which
assesses damaged and occluded objects, too. The integration and
exploitation of different data sources, e.g. vector and image data,
was discussed in several other contributions (Baltsavias, 2004,
Butenuth et al., 2007). However, there is a lack of methods which
assess the GIS data concerning its functionality using imagery
(Morain and Kraft, 2003).
In this paper, a classification system using remote sensing data
and additionally available information is developed to assess GIS-
objects. The main goal of the system is the automatic classifica
tion and evaluation of infrastructure objects, for example the traf
ficability of the road network after natural disasters. However,
the presented system can be transferred to other scenarios, such
as changes in vegetation, because its design is modular. A focus
is the integrated utilization of any available information, which
is important to ease and speed up the classification process with
the aim to derive complete and reliable results (Reinartz et al.,
2003, Frey and Butenuth, 2009). In comparison to the manual
interpretation of images the presented systems is very efficient,