technical development for automatic aerial triangulation of high resolution satellite imagery

326 Pages·2009·9.86 MB·English

TECHNICAL DEVELOPMENT
FOR AUTOMATIC AERIAL
TRIANGULATION OF HIGH
RESOLUTION SATELLITE
IMAGERY
ZHEN XIONG
May 2009
TECHNICAL REPORT
NO. 268
TECHNICAL DEVELOPMENT FOR
AUTOMATIC AERIAL TRIANGULATION OF
HIGH RESOLUTION SATELLITE IMAGERY

Zhen Xiong

Department of Geodesy and Geomatics Engineering
University of New Brunswick
P.O. Box 4400
Fredericton, N.B.
Canada
E3B 5A3

May 2009

© Zhen Xiong 2009
PREFACE

This technical report is a reproduction of a dissertation submitted in partial fulfillment
of the requirements for the degree of Doctor of Philosophy in the Department of Geodesy
and Geomatics Engineering, May 2009. The research was supervised by Dr. Yun Zhang,
and funding was provided by the Natural Sciences and Engineering Research Council of
Canada and by the Canada Research Chairs Program.
As with any copyrighted material, permission to reprint or quote extensively from this
report must be received from the author. The citation to this work should appear as
follows:

Xiong, Zhen (2009). Technical Development for Automatic Aerial Triangulation of
High Resolution Satellite Imagery. Ph.D. dissertation, Department of Geodesy
and Geomatics Engineering, Technical Report No. 268, University of New
Brunswick, Fredericton, New Brunswick, Canada, 302 pp.
ABSTRACT

Because they contain abundant spatial information, high resolution satellite images are
widely used in a variety of applications. Aerial triangulation is one of the most important
technologies to obtain accurate spatial information from those images. Thus aerial
triangulation is always an important research topic in the photogrammetric community
and automatic aerial triangulation is a common goal of such PhD research activities. To
date, many techniques have been developed to improve the efficiency and accuracy of
aerial triangulation. However, for processing high resolution satellite images, automatic
aerial triangulation still faces many challenges, including tie point extraction and sensor
model refinement. The main purpose of this research is to develop and test new tie point
extraction, sensor model refinement and bundle block adjustment methods for improving
the automation and accuracy of aerial triangulation.

The accuracy of tie points directly determines the success of aerial triangulation.
Generally both the corner point and the gravity center point of a rectangular or circular
object can be used as tie points, but the resulting outcomes can vary greatly in aerial
triangulation. However, this difference has not drawn much attention from researchers
yet. Thus, most of the tie point extraction algorithms only extract various corners. In
order to quantify the difference between corner and center tie points for image
registration, this research analyzed the error introduced by using corner or center tie
points in different cases. Through quantitative analysis and experiments, the author
IV
reached the conclusion that the ‘center’ points, when used as tie points, can improve the
accuracy of image registration by at least 40 percent over that for the ‘corner’ points.

Extracting a large number of tie points is the prerequisite of automatic aerial
triangulation. Interest point matching can extract tie points automatically. To date
numerous interest point matching algorithms have been investigated. Those algorithms
can be grouped into two categories: area based and feature based. However, both area
based and feature based algorithms share a common limitation: ambiguity in a
homogeneous area. Neither of the methods could efficiently extract tie points from the
low texture area. In this research, a robust interest point matching algorithm has been
developed. This algorithm incorporates spatial information through constructing a
control network from ‘super’ interest points. Experiments show that the proposed
algorithm almost solved the ambiguity problem in a “poorly textured” area.

Sensor model refinement is the core of aerial triangulation. The challenge is the use of
the Rational Polynomial Camera (RPC) model in some high resolution satellites, such as
IKONOS and QuickBird. Although some direct methods and indirect methods have
been investigated, they either require excessive information concerning the RPC which
is unavailable to the public (direct methods), or has rigorous conditions which seriously
limits its applications (indirect methods). In this research, a generic method was
developed for RPC refinement. The proposed method does not need any information
about the RPC itself, and is not restrained by any conditions. Theoretically, the proposed
generic method can be used in any kind of camera in which RPC is used as a sensor
model.
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Based on the proposed generic method for RPC refinement, a robust bundle block
adjustment model is developed. This bundle block adjustment algorithm can efficiently
process the high resolution satellite images and can reach sub-pixel accuracy in image
space and sub-meter accuracy in object space. Experiments were conducted to verify this
application.

VI
ACKNOWLEDGEMENTS

In the past several years, numerous people gave me strong support and encouragement.
First of all, I would like to thank my supervisor, Dr. Yun Zhang, for his all-around
support and guidance throughout my Ph.D program. During my program of study, he
gave me many opportunities for growth by allowing me to be involved in projects,
conferences, and other academic activities. For my technical reports, journal papers, and
other presentations, he always gave me valuable comments and suggestions. Thank you,
Dr. Zhang.

I am very grateful to Dr. David J. Coleman and Dr. Susan E. Nichols for their help in my
minor examination, for their review of my Ph.D research proposal and dissertation, and
for their valuable comments and suggestions. I thank Dr. Julian Meng for reviewing my
Ph.D research proposal and providing helpful comments. I thank Dr. Costas Amenakis
for reviewing my thesis and giving me valuable comments and suggestions. Thanks are
also extended to the staff and faculty of the Department of Geodesy and Geomatics
Engineering, in particular David Fraser, Terry Arsenault, Jay Woodyer, Sylvia Whitaker,
Kim Delorey, Lorry Hunt and Michelle Ryan for their secretarial, advisory and technical
support.

I want to thank my friends David Whyte for reviewing my papers, Dale Bray, Clark
Hamilton, and Dennis Muehlhauser for helping me with my English. Special thanks
must go to many friends and colleagues for their valuable advice, comments, support,
VII
encouragement, and friendship at various stages of the long journey towards completing
this Ph.D program. They are Dr. Gong Hong, Shen Gao, Jianfeng Zhao, Gensheng
Liang, Xiaolun Yi, Saili Tang, Tristan Goulden, Yongdae Gweon, and Robert Kingdon.

Finally I would like to thank my wife, Ying Ding, and my daughter Katherine, for their
unconditional love, support, and understanding. I am truly grateful to my father, Youcai
Xiong, my mother Qiaozhi Zhang, my father-in-law Chaoming Ding, and my mother-in-
law Meijuan Dong for standing with me during my Ph.D program study.

VIII
TABLE OF CONTENTS

PREFACE ........................................................................................................................ III 
ABSTRACT .................................................................................................................... IV 
TABLE OF CONTENTS ................................................................................................ IX 
LIST OF TABLES ......................................................................................................... XII 
LIST OF FIGURES ....................................................................................................... XV 
LIST OF SYMBOLS, NOMENCLATURE, OR ABBREVIATIONS ...................... XXII 
Chapter 1 INTRODUCTION ............................................................................................. 1 
1.1 Dissertation Structure ............................................................................................... 2 
1.2 Background .............................................................................................................. 2 
1.3 Selection of Research Topics ................................................................................... 5 
1.3.1 Interest Point Extraction .................................................................................... 5 
1.3.2 Interest Point Matching ..................................................................................... 6 
1.3.3 Sensor Model Refinement ................................................................................. 7 
1.3.4 Bundle Block Adjustment ................................................................................. 8 
1.4 Review of Existing Solution .................................................................................... 9 
1.4.1 Interest Point Extraction .................................................................................... 9 
1.4.2 Interest Point Matching ................................................................................... 11 
1.4.3 Sensor Model Refinement ............................................................................... 13 
1.4.4 Bundle Adjustment ......................................................................................... 14 
1.5 Problem Statement ................................................................................................. 15 
1.6 Research Objective................................................................................................. 17 
1.7 Overview of Each Chapter ..................................................................................... 20 
REFERENCES ............................................................................................................. 21 
Chapter 2 ERROR ANALYSIS OF CORNER AND CENTER POINTS
FOR IMAGE REGISTRATION ..................................................................... 26 
ABSTRACT ................................................................................................................. 26 
2.1. Introduction ........................................................................................................... 27 
2.2. Position Errors of Corners and Gravity Centers for .............................................. 28 
Image Registration ....................................................................................................... 28 
IX
2.2.1 Corners and Gravity Centers Used for Registration of Images
with Different Resolutions............................................................................... 30 
2.2.2 Corners and Gravity Centers Used for Registration of Images
Having the Same Resolution ........................................................................... 34 
2.3. Experiment ............................................................................................................ 40 
2.4. Analysis and Conclusions ..................................................................................... 54 
ACKNOWLEDGMENTS ........................................................................................... 55 
REFERENCES ............................................................................................................. 55 
Chapter 3 A NOVEL INTEREST POINT MATCHING ALGORITHM FOR HIGH
RESOLUTION SATELLITE IMAGES .......................................................... 58 
ABSTRACT ................................................................................................................. 58 
3.1 Introduction ............................................................................................................ 59 
3.2 Literature Review ................................................................................................... 60 
3.3 Methodology .......................................................................................................... 65 
3.3.1 Super Point Detection ..................................................................................... 65 
3.3.2 Super Point Matching ...................................................................................... 68 
3.3.3 Interest Point Matching ................................................................................... 73 
3.3.4 Threshold Selection ......................................................................................... 75 
3.4 Experiments ........................................................................................................... 78 
(1)  Test Data 1: ...................................................................................................... 78 
(2)  Test Data 2: ...................................................................................................... 81 
(3)  Test Data 3: ...................................................................................................... 84 
(4)  Test Data 4: ...................................................................................................... 88 
3.5 Conclusions ............................................................................................................ 94 
ACKNOWLEDGEMENTS ......................................................................................... 95 
REFERENCES ............................................................................................................. 95 
Chapter 4 A GENERIC METHOD FOR RPC REFINEMENT USING GROUND
CONTROL INFORMATION ......................................................................... 99 
ABSTRACT ................................................................................................................. 99 
4.1 Introduction .......................................................................................................... 100 
4.2 Outline of RPC Refinement Methods .................................................................. 103 
4.2.1 Indirect methods ............................................................................................ 104 
4.2.2 Direct Methods .............................................................................................. 106 
4.2.3 Limitation of Traditional Methods ................................................................ 108 
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