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

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 104 which is essential in crisis scenarios. Depending on the type and complexity of the input data, 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. Section 2 describes the generic near-realtime classification sys tem with the objective to classify and evaluate objects using re mote sensing and other available data. In Section 3 the system is applied to road objects in case of natural disasters. Two test sce narios of flooded areas are used to verify the system. By means of manually generated reference data, the applicability and effi ciency of the system is evaluated in Section 4. Finally, further investigations in future work are pointed out. 2 CLASSIFICATION SYSTEM The fusion of multi-sensor images is an important issue, because the corregistration between optical and radar images is still a cur rent research topic (Pohl and Van Genderen, 1998). Methods such as mutual information can be applied for the system (Inglada and Giros, 2004). The system has to deal with multi-temporal images having the possibility to derive important information on time. This leads to an even more complex corregistration pro cess. Change detection algorithms can provide information about the variation of assessed objects. In this article the temporal fac tor is neglected, but will be an essential part in future research. The main core of the system represents the classification. The goal is to classify each object into a different state Si. For each object probabilities are derived belonging to a certain state. The methods estimating the probabilities depends on the data: typ ical examples are multispectral classification or fuzzy member ship functions (Figure 2). The goal of the developed classification system is the assessment of GIS-objects using up-to-date remote sensing data. The system is designed in a general and modular way to provide the opportu nity to label GIS-objects into different states. Typical states de scribe the functionality of infrastructure objects as roads or build ings. The generic system embeds different kinds of image data: multi-sensor as well as multi-temporal data. Additionally, any kinds of available data sources and spatial knowledge, which con tributes information for the assessment, can be embedded. Typi cal examples are digital elevation models (DEM) and further G1S information, e.g. land cover or waterways. The minimum re quirement of the system are the objects to be assessed and one up-to-date image which provides the information for the assess ment. Time Point t-| Optical Imagery 1 SAR Imagery 1 DEM ► GIS- object 1 Classification System GIS ► I Optical Imagery 2 Time SAR Imagery 2 Classification System GIS- Object I data 1 ► method 1 - —► PlIlSi) Pil:S;> ••• > Pd,S, data 2 ► method 2 - —► Pd.Sp Pd:S;r ■■■ t Pd;S! ... — - - —► ■■ data n ► method n —► PaSh PJ„S:> ••• ) Pd& Figure 2: Derivation of probabilities from data using various methods Beside the derivation of the individual probabilities from each data source the combination plays a decisive role: Psi =Pd 1 ,s 1 ®Pd 2 ,s 1 <S> • • • <8>Pd n ,s x Ps 2 = Pd 1 ,s 2 <S> Pd 2 ,s 2 ® ■ ■ ■ ® Pd n ,s 2 : (1) PSi = Pd 1 .Si ® Pd 2 .Si ® • • • <8> Pd n ,Si■ The variable Pd n ,Si denotes the probability that the state Si oc curs given data d n ■ The indices i and n describe the number of available states and data, respectively. The result pSi shows the probability that a GIS-object belongs to the state S',-. For each type of data weights w n can be introduced in order to cope with the different influence of infonnation content. Hence, Equation 1 for one state i leads to: i up-to-date map Figure 1 : Classification system PSi = m • Pdi,Si ® • • • <8> w n • Pd n ,Si■ (2) Finally, the object is assigned to the state S, with the largest prob ability pSi. A basic characteristic of the whole system is the combination at the probability level in order to remain flexible concerning the available data. The classification system depicted in Figure 1 can be subdivided into different components. Starting point are the GIS-objects to be assessed. Secondly, the input data as imagery or digital eleva tion models which contribute the information for the assessment. In the following this information is called data. Thirdly, the clas sification system by itself and, finally, a resulting up-to-date map. 3 MODEL FOR ROAD OBJECTS After describing the generic system, a model is shown which as sesses linear objects as roads after flooding. However, this model is transferable to other linear objects like railways and further

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