CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: CLASSIFICATION SYSTEM OF GIS-OBJECTS USING MULTI-SENSORIAL IMAGERY FOR NEAR-REALTIME DISASTER MANAGEMENT Daniel Frey and Matthias Butenuth

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 103 CLASSIFICATION SYSTEM OF GIS-OBJECTS USING MULTI-SENSORIAL IMAGERY FOR NEAR-REALTIME DISASTER MANAGEMENT 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 ABSTRACT: 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 investigations. 1 INTRODUCTION 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,

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