Automatic Extraction of Man-Made Objects from Aerial and Space Images

323 Pages·1995·12.63 MB·English

by  Thomas M. Strat (auth.)

M 0 n t e Proceedings of the Centro Stefano Franscini
Verita Ascona
Edited by K. Osterwalder, ETH Zurich
Automatic Extraction of
Man-Made Objects from
Aerial and Space Images
Edited by
A. Gruen
O. Kuebler
P. Agouris
1995 Birkhauser Verlag
Basel· Boston· Berlin
Editors:
Prof. Dr. Armin Gruen Prof. Dr. Olaf Kuebler
Institute of Geodesy &P hotogrammetry Communications Technology Laboratory
ETH·Hoenggerberg ETH·Zentrum
CH·8093 Zurich CH·8092 Zurich
Dr. Peggy Agouris
Institute of Geodesy & Photogrammetry
ETH-Hoenggerberg
CH·8093 Zurich
e-mail: [email protected]
A CIP catalogue record for this book is available from the Library of Congress, Washington D.C., USA
Deutsche Bibliothek Cataloging·in·Publication Data
Automatic extraction of man· made objects from aerial and
space images / ed. by A. Gruen ... - Basel; Boston; Berlin:
Birkhauser, 1995
(Monte Verit!!)
ISBN-13: 978-3-0348-9958-1 e-ISBN-13: 978-3-0348-9242-1
DOl 10.1007/978-3-0348-9242-1
NE: Gruen, Armin [Hrsg.]
e-mail: [email protected]
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned,
specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on
microfilms or in other ways, and storage in data banks. For any kind of use, permission of the copyright owner must
be obtained.
© 1995 Birkhiiuser Verlag, P.O. Box 133, CH·4010 Basel, Switzerland
Printed on acid-free paper produced from chlorine·free pulp. 00
Softcover reprint of the hardcover 1st edition 1995
987654321

Preface
The idea for this Workshop originated in discussions we had during the early stages of
our joint research project AMOBE (Automation of Digital Terrain Model Generation and
Man-Made Qb.iect Extraction from Aerial Images). One of the objectives of this project
is to combine the expertise of our photogrammetry and computer vision groups, which in
essence translates to supporting computer vision activities with the quantitative ap
proaches of photogrammetry, including precise calibration methods and camera models
and a general 3-D world and analysis concept. In return, it was anticipated that photo
grammetric "know-how" would be amended by the most recent image analysis algo
rithms provided by the computer vision community. It seemed to us that this was a most
appropriate strategy to tackle the difficult problem of automation in Digital Terrain Model
generation and building/road extraction.
It soon became clear to us that it would be highly beneficial, in order to get first-hand in
formation about the state-of-the-art of this quickly developing area, to bring together the
worldwide leading experts of this field. With Monte VeritA in Ascona as the location and
Centro Stefano Franscini as the administrative unit, the Federal Institute of Technology
(ETH) Zurich has at its disposal ideal facilities to conduct a workshop aiming at critical
introspection without completely losing contact with the lovely outside surroundings.
Seeking to provide a forum to outline the advantages and limitations of existing and pro
posed approaches as well as to furnish guidelines for future research, we invited the prin
cipal groups worldwide which, according to our information, had proven and significant
experience and expertise in building and road extraction.
In retrospect, we feel that our Workshop was truly successful. First reactions of the par
ticipants indicate that the aims of the meeting were fully reached. Concentrated and the
matically focused scientific-technical presentations and discussions were supported and
intensified by the pleasure of working and living in a scenic environment and the oppor
tunity to get personally acquainted with colleagues in a relaxing and cozy atmosphere.
It is our hope and desire that something of this unique atmosphere and high quality of our
Workshop will be preserved by these Proceedings. We have structured the contents of this
volume into four major themes, namely General Strategies, Building Extraction, Road
Extraction, Map-Based Extraction, as they reflect the key issues of the Workshop.
We would like to thank all those individuals who contributed to the organization and ad
ministration of this Workshop, in particular Dr. Emmanuel Baltsavias, Dr. Scott Mason,
and Mr. Haihong Li. We also acknowledge the excellent support provided by the Centro
Franscini through Mrs. Katia Bastianelli. Above all, however, we are grateful for the en
during work and patient attitude of Dr. Peggy Agouris, our Workshop scientific manager.
Armin Gruen Oln! Kuebler
Institute of Geodesy and Photogrammetry Communications Technology Laboratory
Table of Contents
General Strategies
Using Context to Control Computer Vision Algorithms
Thomas M. Strat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3
Inferring Homogeneous Regions from Rich Image Attributes
OlofHenricsson .................................................. 13
Stereo Reconstruction from Multiple Views
Pascal Fua ...................................................... 23
Model Registration and Validation
Andres Huertas, Mathias Bejanin, Ramakant Nevatia .................... 33
Automatic Matching of 3-D Models to Imagery
Walter Mueller, James Olson ........................................ 43
Structural 3D-Analysis of Aerial Images with a Blackboard-Based Production
System
Uwe Stilla, Eckart Michaelsen, Karl Lutjen ............................ 53
Semi-Automatic Detection and Extraction of Man-Made Objects in
Multispectral Aerial and Satellite Images
Vittala K. Shettigara, Siegfried G. Kempinger, Robert Aitchison ............ 63
Information Extraction from Digital Images -A KTH Approach
Eberhard Gulch .................................................. 73
Linear Feature Extraction with Dynamic Programming and Globally Enforced
Least Squares Matching
Armin Gruen, Peggy Agouris, Haihong Li ............................. 83
Semi-Automatic Feature Extraction by Snakes
John Trinder, Haihong Li ........................................... 95
From Ziplock Snakes to Velcro™ Surfaces
Walter Neuenschwander, Pascal Fua, Gabor Szekely, Olaf Kubler. . . . . . . . .. 105
Building Extraction
A Layered Abduction Model of Building Recognition
Toni Schenk .................................................... 117
Detection of Buildings from Monocular Images
Chungan Lin, Andres Huertas, Ramakant Nevatia ...................... 125
High-Resolution Stereo for the Detection of Buildings
Marc Berthod, L. Gabet, Gerard Giraudon, Jean-Luc Lotti ............... 135
3-D Reconstruction of Urban Scenes from Sequences of Images
Olivier Faugeras, Stephane Laveau, Luc Robert, Gabriella Csurka,
Cyril Zeller. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 145
Automatic Extraction of Buildings and Terrain from Aerial Images
Robert T. Collins, Allen R. Hanson, Edward M. Riseman, Howard Schultz ... 169
Mid-Level Vision Processes for Automatic Building Extraction
Wolfgang Foerstner .............................................. 179
Geometric Versus Texture Detail in 3-D Models of Real World Buildings
Michael Gruber, Marko Pasko, Franz Leberl ........................... 189
Use of DTMslDSMs and Orthoimages to Support Building Extraction
Emmanuel Baitsavias, Scott Mason, Dirk Stallmann ..................... 199
Data Fusion for the Detection and Reconstruction of Buildings
Norbert Haala, Michael Hahn ...................................... 211
Building Extraction and Verification from Spaceborne and Aerial Imagery
Using Image Understanding Fusion Techniques
Taejung Kim, lan-Peter Muller ...................................... 221
Building Extraction from Stereo Pairs of Aerial Images: Accuracy and
Productivity Constraint of a Topographic Production Line
Olivier lamet, Olivier Dissard, Sylvain Airault ......................... 231
Road Extraction
Tracking Roads in Satellite Images by Playing Twenty Questions
Bruno ledynak,lean-Philippe Roze .................................. 243
New Geometric Stochastic Technology for Finding and Recognizing Roads and
Their Features in Aerial Images
Meir Barzohar, David B. Cooper .................................... 255
Road Tracing by Profile Matching and Kalman Filtering
George Vosselman,lurrien de Knecht ................................ 265
Model-Based Road Extraction from Images
Carsten Steger, Clemens Glock, Wolfgang Eckstein, Helmut Mayer,
Bernd Radig .................................................... 275
Map-Based Extraction
Automatic Extraction and Structuring of Objects from Scanned Topographical
Maps -An Alternative to the Extraction from Aerial and Space Images?
Stephan Nebiker, Alessandro Carosio ................................. 287
Cooperative Use of Aerial Images and Maps for the IDterpretation of Urban
Scenes
Henri Maitre, Isabelle Bloch, Henri Moissinac, Christophe Gouinaud ....... 297
Map-Based Semantic Modeling for the Extraction of Objects from Aerial
Images
Franz Quint, Manfred Sties ......................................... 307
List of Workshop Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 317
Author Index ....................................................... 321
General Strategies
3
Automatic Extraction of Man-Made Objects
from Aerial and Space Images
Monte Verita, © 1995 Birkhauser Verlag Basel
Using Context to Control Computer Vision
Algorithms
Thomas M. Strat
Artificial Intelligence Center
SRI International
333 Ravenswood Avenue
Menlo Park, California 94025, USA
[email protected]
Abstract
We are investigating the design of an architecture that can be used as the basis for
controlling the invocation of image understanding algorithms for cartographic feature
extraction. The key research question is whether sufficient contextual constraints are
available to choose algorithms and their parameters for aerial photo analysis. Our
approach has been to apply the context-based architecture incorporated in CONDOR,
an SRJ system for automatically constructing scene models of natural terrain from
ground-level views. The semiautomated nature of cartographic feature extraction
allows access to additional sources of contextual constraints that were not available to
CONDOR. 1
1 Introduction
Thirty years of research in image understanding (IU) have produced an enormous number
of computer vision algorithms, many of which have demonstrated reliable performance
at solving particular tasks in restricted domains. However, the development of computer
vision systems that are reliable in more general circumstances has proved elusive [JB91].
It is in response to this situation that a framework is offered within which computer
vision algorithms of specialized competence can be used and integrated with other such
algorithms to produce a reliable vision system that operates effectively in a broader context
than any of its individual component algorithms can. Other authors have also stressed
the use of context to aid image interpretation [Alo90, CGHS89, FHS94].
The IU system that we envision comprises a large number of computer vision algo
rithms, each tailored to accomplish a particular task under a particular set of circum
stances. The goals of these algorithms may overlap or even duplicate each other's goals,
but the assumptions that they make and the data with which they operate will often differ.
The strategy for integrating such a collection of computer vision algorithms is to rep
resent explicitly the assumptions made by each algorithm, and to use the context of the
present task to select the most appropriate algorithms for solving that task. By doing
1 The research reported here was supported by the Advanced Research Projects Agency and the U.S.
Army Topographic Engineering Center under Contract DACA 76-92-C-0034.