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CMRT09

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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:: TRAJECTORY-BASED SCENE DESCRIPTION AND CLASSIFICATION BY ANALYTICAL FUNCTIONS D. Pfeiffer, R. Reulke

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

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 
Paris, France, 3-4 September, 2009 
A rectangle classifier and a modified k nearest neighbours 
(KNN-) classifier are used. The result of the classification 
shall be unambiguously. 
k-nearest neighbours algorithm (KNN) is a method for classi 
fying objects based on closest training examples in the fea 
ture space. 
A data set of 414 different trajectories (Total) has been proc 
essed using different functions within the test data set. A total 
of 62 trajectories could not be classified (NC). A summary is 
given in table 2. 
The results shall be represented in greater detail by the hy 
perboles in the following. 
The rectangle classification (also cuboid classification) is a 
distribution free, nonparametric and supervised classification 
method (see figure 4). 
A 
V 
2 
Klaise 2 
i ** 
Merkma; 1 ^ 
Figure 4. A simple rectangle classification in a 2D feature 
space 
The KNN classification needs a training data set. It is a non 
parametric method for the estimate of probability densities. 
The operation of the classifier is steered by k (number of 
regarded neighbours, a free selectable parameter) and 8 (used 
metric). Figure 5 shows the approach. 
© 
& 
■M A 
© ® 
Figure 5. Visualization of the KNN classification. The k=7 
nearest neighbour are used. The object g is as 
signed to the class B 
The metric 5 defines the reliable determination of the dis 
tances to adjacent elements. The result of the classification 
depends substantially on the density of the learning set and 
the choice of the metric. Here the Mahalanobis distance was 
used. 
4. RESULTS 
To- 
N 
WR 
WR 
R 
R 
RW 
RW 
tal 
C 
R 
L 
O 
W 
R 
L 
Ref 
414 
62 
117 
59 
72 
26 
33 
54 
Circ 
414 
62 
119 
59 
68 
21 
34 
51 
Elli 
410 
51 
117 
58 
72 
28 
34 
50 
Hyp 
410 
51 
117 
58 
72 
28 
34 
50 
Str 
413 
50 
125 
56 
70 
28 
35 
49 
Table 2. Summary of complete occurrence and the class oc 
currence of different trajectory types. Ref - refer 
ence, Circ - circle, Elli - ellipse, Hyp - hyper 
bola, Str - straight lines 
4.1 Hyperbola 
Figure 6 shows examples of the approximation of hyperbo 
les. 
Figure 6. Approximation of hyperboles 
In addition to the parameters of the conical sections the di 
rection of motion was uses for the classification. Figure 7 
shows the plot of the rotational angle cp (X) and the delta in 
degrees (<J>) where the trajectory adapts to the hyperbola: 
Figure 7. Classification results 
Class 
Cuboid Classifier 
KNN-Classifier 
Total 
92.9% 
97.8% 
No_Class 
84.3% 
98.0% 
WRR 
95.7% 
99.1% 
WRL 
93.1% 
99.0% 
RO 
97.2% 
99.5% 
RW 
96.4% 
96.4% 
RWR 
85.3% 
91.2% 
WRL 
92.00% 
94.0%

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