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CMRT09

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Bibliographic data

fullscreen: CMRT09

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:
RAY TRACING AND SAR-TOMOGRAPHY FOR 3D ANALYSIS OF MICROWAVE SCATTERING AT MAN-MADE OBJECTS S. Auer, X. Zhu, S. Hinz, R. Bamler
Document type:
Monograph
Structure type:
Chapter

Contents

Table of contents

  • CMRT09
  • Cover
  • ColorChart
  • Title page
  • Workshop Committees
  • Program Committee:
  • Preface
  • Contents
  • EFFICIENT ROAD MAPPING VIA INTERACTIVE IMAGE SEGMENTATION O. Barinova, R. Shapovalov, S. Sudakov, A. Velizhev, A. Konushin
  • SURFACE MODELLING FOR ROAD NETWORKS USING MULTI-SOURCE GEODATA Chao-Yuan Lo, Liang-Chien Chen, Chieh-Tsung Chen, and Jia-Xun Chen
  • AUTOMATIC EXTRACTION OF URBAN OBJECTS FROM MULTI-SOURCE AERIAL DATA Adriano Mancini, Emanuele Frontoni and Primo Zingaretti
  • ROAD ROUNDABOUT EXTRACTION FROM VERY HIGH RESOLUTION AERIAL IMAGERY M. Ravenbakhsh, C. S. Fraser
  • ASSESSING THE IMPACT OF DIGITAL SURFACE MODELS ON ROAD EXTRACTION IN SUBURBAN AREAS BY REGION-BASED ROAD SUBGRAPH EXTRACTION Anne Grote, Franz Rottensteiner
  • VEHICLE ACTIVITY INDICATION FROM AIRBORNE LIDAR DATA OF URBAN AREAS BY BINARY SHAPE CLASSIFICATION OF POINT SETS W. Yaoa, S. Hinz, U. Stilla
  • TRAJECTORY-BASED SCENE DESCRIPTION AND CLASSIFICATION BY ANALYTICAL FUNCTIONS D. Pfeiffer, R. Reulke
  • 3D BUILDING RECONSTRUCTION FROM LIDAR BASED ON A CELL DECOMPOSITION APPROACH Martin Kada, Laurence McKinle
  • A SEMI-AUTOMATIC APPROACH TO OBJECT EXTRACTION FROM A COMBINATION OF IMAGE AND LASER DATA S. A. Mumtaz, K. Mooney
  • COMPLEX SCENE ANALYSIS IN URBAN AREAS BASED ON AN ENSEMBLE CLUSTERING METHOD APPLIED ON LIDAR DATA P. Ramzi, F. Samadzadegan
  • EXTRACTING BUILDING FOOTPRINTS FROM 3D POINT CLOUDS USING TERRESTRIAL LASER SCANNING AT STREET LEVEL Karim Hammoudi, Fadi Dornaika and Nicolas Paparoditis
  • DETECTION OF BUILDINGS AT AIRPORT SITES USING IMAGES & LIDAR DATA AND A COMBINATION OF VARIOUS METHODS Demir, N., Poli, D., Baltsavias, E.
  • DENSE MATCHING IN HIGH RESOLUTION OBLIQUE AIRBORNE IMAGES M. Gerke
  • COMPARISON OF METHODS FOR AUTOMATED BUILDING EXTRACTION FROM HIGH RESOLUTION IMAGE DATA G. Vozikis
  • SEMI-AUTOMATIC CITY MODEL EXTRACTION FROM TRI-STEREOSCOPIC VHR SATELLITE IMAGERY F. Tack, R. Goossens, G. Buyuksalih
  • AUTOMATED SELECTION OF TERRESTRIAL IMAGES FROM SEQUENCES FOR THE TEXTURE MAPPING OF 3D CITY MODELS Sébastien Bénitez and Caroline Baillard
  • CLASSIFICATION SYSTEM OF GIS-OBJECTS USING MULTI-SENSORIAL IMAGERY FOR NEAR-REALTIME DISASTER MANAGEMENT Daniel Frey and Matthias Butenuth
  • AN APPROACH FOR NAVIGATION IN 3D MODELS ON MOBILE DEVICES Wen Jiang, Wu Yuguo, Wang Fan
  • GRAPH-BASED URBAN OBJECT MODEL PROCESSING Kerstin Falkowski and Jürgen Ebert
  • A PROOF OF CONCEPT OF ITERATIVE DSM IMPROVEMENT THROUGH SAR SCENE SIMULATION D. Derauw
  • COMPETING 3D PRIORS FOR OBJECT EXTRACTION IN REMOTE SENSING DATA Konstantinos Karantzalos and Nikos Paragios
  • OBJECT EXTRACTION FROM LIDAR DATA USING AN ARTIFICIAL SWARM BEE COLONY CLUSTERING ALGORITHM S. Saeedi, F. Samadzadegan, N. El-Sheimy
  • BUILDING FOOTPRINT DATABASE IMPROVEMENT FOR 3D RECONSTRUCTION: A DIRECTION AWARE SPLIT AND MERGE APPROACH Bruno Vallet and Marc Pierrot-Deseilligny and Didier Boldo
  • A TEST OF AUTOMATIC BUILDING CHANGE DETECTION APPROACHES Nicolas Champion, Franz Rottensteiner, Leena Matikainen, Xinlian Liang, Juha Hyyppä and Brian P. Olsen
  • CURVELET APPROACH FOR SAR IMAGE DENOISING, STRUCTURE ENHANCEMENT, AND CHANGE DETECTION Andreas Schmitt, Birgit Wessel, Achim Roth
  • RAY TRACING AND SAR-TOMOGRAPHY FOR 3D ANALYSIS OF MICROWAVE SCATTERING AT MAN-MADE OBJECTS S. Auer, X. Zhu, S. Hinz, R. Bamler
  • THEORETICAL ANALYSIS OF BUILDING HEIGHT ESTIMATION USING SPACEBORNE SAR-INTERFEROMETRY FOR RAPID MAPPING APPLICATIONS Stefan Hinz, Sarah Abelen
  • FUSION OF OPTICAL AND INSAR FEATURES FOR BUILDING RECOGNITION IN URBAN AREAS J. D. Wegner, A. Thiele, U. Soergel
  • FAST VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE BURSTS Karsten Kozempel and Ralf Reulke
  • REFINING CORRECTNESS OF VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE SEQUENCES BY MEANS OF VELOCITY AND TRAJECTORY EVALUATION D. Lenhart, S. Hinz
  • UTILIZATION OF 3D CITY MODELS AND AIRBORNE LASER SCANNING FOR TERRAIN-BASED NAVIGATION OF HELICOPTERS AND UAVs M. Hebel, M. Arens, U. Stilla
  • STUDY OF SIFT DESCRIPTORS FOR IMAGE MATCHING BASED LOCALIZATION IN URBAN STREET VIEW CONTEXT David Picard, Matthieu Cord and Eduardo Valle
  • TEXT EXTRACTION FROM STREET LEVEL IMAGES J. Fabrizio, M. Cord, B. Marcotegui
  • CIRCULAR ROAD SIGN EXTRACTION FROM STREET LEVEL IMAGES USING COLOUR, SHAPE AND TEXTURE DATABASE MAPS A. Arlicot, B. Soheilian and N. Paparoditis
  • IMPROVING IMAGE SEGMENTATION USING MULTIPLE VIEW ANALYSIS Martin Drauschke, Ribana Roscher, Thomas Läbe, Wolfgang Förstner
  • REFINING BUILDING FACADE MODELS WITH IMAGES Shi Pu and George Vosselman
  • AN UNSUPERVISED HIERARCHICAL SEGMENTATION OF A FAÇADE BUILDING IMAGE IN ELEMENTARY 2D - MODELS Jean-Pascal Burochin, Olivier Tournaire and Nicolas Paparoditis
  • GRAMMAR SUPPORTED FACADE RECONSTRUCTION FROM MOBILE LIDAR MAPPING Susanne Becker, Norbert Haala
  • Author Index
  • Cover

Full text

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
160 
• slice no. 3 for displaying intensities in elevation 
direction 
According to the defined slices, necessary data in slant-range, 
azimuth and elevation are extracted out of the data pool 
provided by the sampling process in POV Ray. 
Slant Range |m| 
Figure 5: Slice 1: elevation heights in slant-range direction 
(slice 1 in Figure 4 corresponds to slant range interval 
60 m to 140 m); blue: single bounce contributions, 
green: double bounce contributions 
-40 -30 -20 -10 O 10 20 30 40 
Azimuth [mj 
Figure 6: Slice 2: elevation information along azimuth direction 
displayed in height over ground; blue: single bounce 
contributions, green: double bounce contributions; 
zero level = level of ground surrounding the step 
Since the incidence angle used for sampling the 3D model 
scene is known, slice no. 1 pointing in slant range direction can 
be presented by two versions, either by displaying elevation 
heights (Figure 5), i.e. elevation coordinates with respect to a 
master height situated in the center of the image plane used for 
sampling the scene, or by providing height information in 
height over ground geometry, i.e. heights with respect to the 
ground surrounding the box. 
Following the slant-range direction from left to right, displaying 
height data in elevation heights enables to distinguish between 
range intervals containing one scatterer and areas containing 
several scatterers resulting in layover effects, which can not be 
separated in reflectivity maps such as shown in Fig. 3 (right). In 
Figure 5, reflection caused by direct backscattering are colored 
in blue color while double bounce contributions are indicated 
by green spots. Due to the incidence angle of 45 degrees, 
double bounce effects are focused at the same position in slant- 
range and are overlaid by both single bounce contributions at 
the ground and single bounce contributions reflected at the end 
of the step up-side. 
Following slice no. 2 along its way, elevation information is 
shown along the azimuth direction in height over ground 
(Figure 6). After passing an interval of contributions directly 
backscattered at the ground, the layover region starts showing 
the width of the double bounce areas in azimuth, which are 
equal to the width of the step model. As expected, double 
bounce contributions caused by the interaction between 
perpendicular faces are concentrated at the corresponding 
intersection lines and, hence, show a height value of 0 and 20 
meters, respectively. 
Figure 7: Slice 3: normalized intensities along elevation 
direction; step width in elevation: 2 meters; blue: 
single bounce contributions, green: double bounce 
contribution 
Slice no. 3 pointing in elevation direction is shown in Figure 7. 
After the spatial sampling along elevation direction is chosen 
by the operator, intensity contributions are assigned to elevation 
intervals and summed up. Since the selected pixel is located 
within the double bounce area of two dihedrals, slice no. 3 
shows two strong double bounce contributions caused by the 
interaction of step faces (colored in green) accompanied by 
weak direct backscattering derived at the step faces (colored in 
blue). Although the radiometric quality of detected intensity 
contributions is moderate due to simplified reflection models 
and the approximation of SAR signals by rays, proportions 
between single and double bounce intensities within one 
resolution cell are well represented. 
In the following Section, simulation results will be compared to 
real data derived by tomographic analysis. 
3. COMPARISON: SIMULATION VS. REAL DATA 
For demonstrating potential applications of SAR simulation in 
elevation dimension, a practical example extracted from 
tomographic analysis using TerraSAR-X high resolution 
spotlight data is provided in this section and compared to 
simulation results. 
3.1 Object modelling 
Fig.8 shows the 2D intensity map for the convention center of 
Las Vegas acquired by TerraSAR-X. For the purpose of this 
paper, an azimuth-range pixel marked by a green dot has been 
taken as example. The complex valued measurement at this 
pixel corresponds to the integration of the reflected radar signal
	        

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