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RemoteSensing Digital Image Analysis.pdf
Preface
Contents
1 Sources and Characteristics of Remote Sensing Image Data
1.1…Energy Sources and Wavelength Ranges
1.2…Primary Data Characteristics
1.3…Remote Sensing Platforms
1.4…What Earth Surface Properties are Measured?
1.4.1 Sensing in the Visible and Reflected Infrared Ranges
1.4.2 Sensing in the Thermal Infrared Range
1.4.3 Sensing in the Microwave Range
1.5…Spatial Data Sources in General and Geographic Information Systems
1.6…Scale in Digital Image Data
1.7…Digital Earth
1.8…How This Book is Arranged
1.9…Bibliography on Sources and Characteristics of Remote Sensing Image Data
1.10…Problems
2 Correcting and Registering Images
2.1…Introduction
2.2…Sources of Radiometric Distortion
2.3…Instrumentation Errors
2.3.1 Sources of Distortion
2.3.2 Correcting Instrumentation Errors
2.4…Effect of the Solar Radiation Curve and the Atmosphere on Radiometry
2.5…Compensating for the Solar Radiation Curve
2.6…Influence of the Atmosphere
2.7…Effect of the Atmosphere on Remote Sensing Imagery
2.8…Correcting Atmospheric Effects in Broad Waveband Systems
2.9…Correcting Atmospheric Effects in Narrow Waveband Systems
2.10…Empirical, Data Driven Methods for Atmospheric Correction
2.10.1 Haze Removal by Dark Subtraction
2.10.2 The Flat Field Method
2.10.3 The Empirical Line Method
2.10.4 Log Residuals
2.11…Sources of Geometric Distortion
2.12…The Effect of Earth Rotation
2.13…The Effect of Variations in Platform Altitude, Attitude and Velocity
2.14…The Effect of Sensor Field of View: Panoramic Distortion
2.15…The Effect of Earth Curvature
2.16…Geometric Distortion Caused by Instrumentation Characteristics
2.16.1 Sensor Scan Nonlinearities
2.16.2 Finite Scan Time Distortion
2.16.3 Aspect Ratio Distortion
2.17…Correction of Geometric Distortion
2.18…Use of Mapping Functions for Image Correction
2.18.1 Mapping Polynomials and the Use of Ground Control Points
2.18.2 Building a Geometrically Correct Image
2.18.3 Resampling and the Need for Interpolation
2.18.4 The Choice of Control Points
2.18.5 Example of Registration to a Map Grid
2.19…Mathematical Representation and Correction of Geometric Distortion
2.19.1 Aspect Ratio Correction
2.19.2 Earth Rotation Skew Correction
2.19.3 Image Orientation to North--South
2.19.4 Correcting Panoramic Effects
2.19.5 Combining the Corrections
2.20…Image to Image Registration
2.20.1 Refining the Localisation of Control Points
2.20.2 Example of Image to Image Registration
2.21…Other Image Geometry Operations
2.21.1 Image Rotation
2.21.2 Scale Changing and Zooming
2.22…Bibliography on Correcting and Registering Images
2.23…Problems
3 Interpreting Images
3.1…Introduction
3.2…Photointerpretation
3.2.1 Forms of Imagery for Photointerpretation
3.2.2 Computer Enhancement of Imagery for Photointerpretation
3.3…Quantitative Analysis: From Data to Labels
3.4…Comparing Quantitative Analysis and Photointerpretation
3.5…The Fundamentals of Quantitative Analysis
3.5.1 Pixel Vectors and Spectral Space
3.5.2 Linear Classifiers
3.5.3 Statistical Classifiers
3.6…Sub-classes and Spectral Classes
3.7…Unsupervised Classification
3.8…Bibliography on Interpreting Images
3.9…Problems
4 Radiometric Enhancement of Images
4.1…Introduction
4.1.1 Point Operations and Look Up Tables
4.1.2 Scalar and Vector Images
4.2…The Image Histogram
4.3…Contrast Modification
4.3.1 Histogram Modification Rule
4.3.2 Linear Contrast Modification
4.3.3 Saturating Linear Contrast Enhancement
4.3.4 Automatic Contrast Enhancement
4.3.5 Logarithmic and Exponential Contrast Enhancement
4.3.6 Piecewise Linear Contrast Modification
4.4…Histogram Equalisation
4.4.1 Use of the Cumulative Histogram
4.4.2 Anomalies in Histogram Equalisation
4.5…Histogram Matching
4.5.1 Principle
4.5.2 Image to Image Contrast Matching
4.5.3 Matching to a Mathematical Reference
4.6…Density Slicing
4.6.1 Black and White Density Slicing
4.6.2 Colour Density Slicing and Pseudocolouring
4.7…Bibliography on Radiometric Enhancement of Images
4.8…Problems
5 Geometric Processing and Enhancement: Image Domain Techniques
5.1…Introduction
5.2…Neighbourhood Operations in Image Filtering
5.3…Image Smoothing
5.3.1 Mean Value Smoothing
5.3.2 Median Filtering
5.3.3 Modal Filtering
5.4…Sharpening and Edge Detection
5.4.1 Spatial Gradient Methods
5.4.1.1 The Roberts Operator
5.4.1.2 The Sobel Operator
5.4.1.3 The Prewitt Operator
5.4.1.4 The Laplacian Operator
5.4.2 Subtractive Smoothing (Unsharp Masking)
5.5…Edge Detection
5.6…Line and Spot Detection
5.7…Thinning and Linking
5.8…Geometric Processing as a Convolution Operation
5.9…Image Domain Techniques Compared with Using the Fourier Transform
5.10…Geometric Properties of Images
5.10.1 Measuring Geometric Properties
5.10.2 Describing Texture
5.11…Morphological Analysis
5.11.1 Erosion
5.11.2 Dilation
5.11.3 Opening and Closing
5.11.4 Boundary Extraction
5.11.5 Other Morphological Operations
5.12…Shape Recognition
5.13…Bibliography on Geometric Processing and Enhancement: Image Domain Techniques
5.14…Problems
6 Spectral Domain Image Transforms
6.1…Introduction
6.2…Image Arithmetic and Vegetation Indices
6.3…The Principal Components Transformation
6.3.1 The Mean Vector and The Covariance Matrix
6.3.2 A Zero Correlation, Rotational Transform
6.3.3 The Effect of an Origin Shift
6.3.4 Example and Some Practical Considerations
6.3.5 Application of Principal Components in Image Enhancement and Display
6.3.6 The Taylor Method of Contrast Enhancement
6.3.7 Use of Principal Components for Image Compression
6.3.8 The Principal Components Transform in Change Detection Applications
6.3.9 Use of Principal Components for Feature Reduction
6.4…The Noise Adjusted Principal Components Transform
6.5…The Kauth--Thomas Tasseled Cap Transform
6.6…The Kernel Principal Components Transformation
6.7…HSI Image Display
6.8…Pan Sharpening
6.9…Bibliography on Spectral Domain Image Transforms
6.10…Problems
7 Spatial Domain Image Transforms
7.1…Introduction
7.2…Special Functions
7.2.1 The Complex Exponential Function
7.2.2 The Impulse or Delta Function
7.2.3 The Heaviside Step Function
7.3…The Fourier Series
7.4…The Fourier Transform
7.5…The Discrete Fourier Transform
7.5.1 Properties of the Discrete Fourier Transform
7.5.2 Computing the Discrete Fourier Transform
7.6…Convolution
7.6.1 The Convolution Integral
7.6.2 Convolution with an Impulse
7.6.3 The Convolution Theorem
7.6.4 Discrete Convolution
7.7…Sampling Theory
7.8…The Discrete Fourier Transform of an Image
7.8.1 The Transformation Equations
7.8.2 Evaluating the Fourier Transform of an Image
7.8.3 The Concept of Spatial Frequency
7.8.4 Displaying the DFT of an Image
7.9…Image Processing Using the Fourier Transform
7.10…Convolution in two Dimensions
7.11…Other Fourier Transforms
7.12…Leakage and Window Functions
7.13…The Wavelet Transform
7.13.1 Background
7.13.2 Orthogonal Functions and Inner Products
7.13.3 Wavelets as Basis Functions
7.13.4 Dyadic Wavelets with Compact Support
7.13.5 Choosing the Wavelets
7.13.6 Filter Banks
7.13.6.1 Sub Band Filtering, and Downsampling
7.13.6.2 Reconstruction from the Wavelets, and Upsampling
7.13.6.3 Relationship Between the Low and High Pass Filters
7.13.7 Choice of Wavelets
7.14…The Wavelet Transform of an Image
7.15…Applications of the Wavelet Transform in Remote Sensing Image Analysis
7.16…Bibliography on Spatial Domain Image Transforms
7.17…Problems
8 Supervised Classification Techniques
8.1…Introduction
8.2…The Essential Steps in Supervised Classification
8.3…Maximum Likelihood Classification
8.3.1 Bayes’ Classification
8.3.2 The Maximum Likelihood Decision Rule
8.3.3 Multivariate Normal Class Models
8.3.4 Decision Surfaces
8.3.5 Thresholds
8.3.6 Number of Training Pixels Required
8.3.7 The Hughes Phenomenon and the Curse of Dimensionality
8.3.8 An Example
8.4…Gaussian Mixture Models
8.5…Minimum Distance Classification
8.5.1 The Case of Limited Training Data
8.5.2 The Discriminant Function
8.5.3 Decision Surfaces for the Minimum Distance Classifier
8.5.4 Thresholds
8.5.5 Degeneration of Maximum Likelihood to Minimum Distance Classification
8.5.6 Classification Time Comparison of the Maximum Likelihood and Minimum Distance Rules
8.6…Parallelepiped Classification
8.7…Mahalanobis Classification
8.8…Non-parametric Classification
8.9…Table Look Up Classification
8.10…kNN (Nearest Neighbour) Classification
8.11…The Spectral Angle Mapper
8.12…Non-Parametric Classification from a Geometric Basis
8.12.1 The Concept of a Weight Vector
8.12.2 Testing Class Membership
8.13…Training a Linear Classifier
8.14…The Support Vector Machine: Linearly Separable Classes
8.15…The Support Vector Machine: Overlapping Classes
8.16…The Support Vector Machine: Nonlinearly Separable Data and Kernels
8.17…Multi-Category Classification with Binary Classifiers
8.18…Committees of Classifiers
8.18.1 Bagging
8.18.2 Boosting and AdaBoost
8.19…Networks of Classifiers: The Neural Network
8.19.1 The Processing Element
8.19.2 Training the Neural Network---Backpropagation
8.19.3 Choosing the Network Parameters
8.19.4 Example
8.20…Context Classification
8.20.1 The Concept of Spatial Context
8.20.2 Context Classification by Image Pre-processing
8.20.3 Post Classification Filtering
8.20.4 Probabilistic Relaxation Labelling
8.20.4.1 The Algorithm
8.20.4.2 The Neighbourhood Function
8.20.4.3 Determining the Compatibility Coefficients
8.20.4.4 Stopping the Process
8.20.4.5 Examples
8.20.5 Handling Spatial Context by Markov Random Fields
8.21…Bibliography on Supervised Classification Techniques
8.22…Problems
9 Clustering and Unsupervised Classification
9.1…How Clustering is Used
9.2…Similarity Metrics and Clustering Criteria
9.3…k Means Clustering
9.3.1 The k Means Algorithm
9.4…Isodata Clustering
9.4.1 Merging and Deleting Clusters
9.4.2 Splitting Elongated Clusters
9.5…Choosing the Initial Cluster Centres
9.6…Cost of k Means and Isodata Clustering
9.7…Unsupervised Classification
9.8…An Example of Clustering with the k Means Algorithm
9.9…A Single Pass Clustering Technique
9.9.1 The Single Pass Algorithm
9.9.2 Advantages and Limitations of the Single Pass Algorithm
9.9.3 Strip Generation Parameter
9.9.4 Variations on the Single Pass Algorithm
9.9.5 An Example of Clustering with the Single Pass Algorithm
9.10…Hierarchical Clustering
9.10.1 Agglomerative Hierarchical Clustering
9.11…Other Clustering Metrics
9.12…Other Clustering Techniques
9.13…Cluster Space Classification
9.14…Bibliography on Clustering and Unsupervised Classification
9.15…Problems
10 Feature Reduction
10.1…The Need for Feature Reduction
10.2…A Note on High Dimensional Data
10.3…Measures of Separability
10.4…Divergence
10.4.1 Definition
10.4.2 Divergence of a Pair of Normal Distributions
10.4.3 Using Divergence for Feature Selection
10.4.4 A Problem with Divergence
10.5…The Jeffries-Matusita (JM) Distance
10.5.1 Definition
10.5.2 Comparison of Divergence and JM Distance
10.6…Transformed Divergence
10.6.1 Definition
10.6.2 Transformed Divergence and the Probability of Correct Classification
10.6.3 Use of Transformed Divergence in Clustering
10.7…Separability Measures for Minimum Distance Classification
10.8…Feature Reduction by Spectral Transformation
10.8.1 Feature Reduction Using the Principal Components Transformation
10.8.2 Feature Reduction Using the Canonical Analysis Transformation
10.8.2.1 Within Class and Among Class Covariance
10.8.2.2 A Separability Measure
10.8.2.3 The Generalised Eigenvalue Equation
10.8.2.4 An Example
10.8.3 Discriminant Analysis Feature Extraction (DAFE)
10.8.4 Non-Parametric Discriminant Analysis (NDA)
10.8.5 Decision Boundary Feature Extraction (DBFE)
10.8.6 Non-Parametric Weighted Feature Extraction (NWFE)
10.9…Block Diagonalising the Covariance Matrix
10.10…Improving Covariance Estimates Through Regularisation
10.11…Bibliography on Feature Reduction
10.12…Problems
11 Image Classification in Practice
11.1…Introduction
11.2…An Overview of Classification
11.2.1 Parametric and Non-parametric Supervised Classifiers
11.2.2 Unsupervised Classification
11.2.3 Semi-Supervised Classification
11.3…Supervised Classification with the Maximum Likelihood Rule
11.3.1 Outline
11.3.2 Gathering Training Data
11.3.3 Feature Selection
11.3.4 Resolving Multimodal Distributions
11.3.5 Effect of Resampling on Classification
11.4…A Hybrid Supervised/Unsupervised Methodology
11.4.1 Outline of the Method
11.4.2 Choosing the Image Segments to Cluster
11.4.3 Rationalising the Number of Spectral Classes
11.4.4 An Example
11.5…Cluster Space Classification
11.6…Supervised Classification Using the Support Vector Machine
11.6.1 Initial Choices
11.6.2 Grid Searching for Parameter Determination
11.6.3 Data Centering and Scaling
11.7…Assessing Classification Accuracy
11.7.1 Use of a Testing Set of Pixels
11.7.2 The Error Matrix
11.7.3 Quantifying the Error Matrix
11.7.4 The Kappa Coefficient
11.7.5 Number of Testing Samples Required for Assessing Map Accuracy
11.7.6 Number of Testing Samples Required for Populating the Error Matrix
11.7.7 Placing Confidence Limits on Assessed Accuracy
11.7.8 Cross Validation Accuracy Assessment and the Leave One Out Method
11.8…Decision Tree Classifiers
11.8.1 CART (Classification and Regression Trees)
11.8.2 Random Forests
11.8.3 Progressive Two-Class Decision Classifier
11.9…Image Interpretation through Spectroscopy and Spectral Library Searching
11.10…End Members and Unmixing
11.11…Is There a Best Classifier?
11.12…Bibliography on Image Classification in Practice
11.13…Problems
12 Multisource Image Analysis
12.1…Introduction
12.2…Stacked Vector Analysis
12.3…Statistical Multisource Methods
12.3.1 Joint Statistical Decision Rules
12.3.2 Committee Classifiers
12.3.3 Opinion Pools and Consensus Theory
12.3.4 Use of Prior Probabilities
12.3.5 Supervised Label Relaxation
12.4…The Theory of Evidence
12.4.1 The Concept of Evidential Mass
12.4.2 Combining Evidence with the Orthogonal Sum
12.4.3 Decision Rules
12.5…Knowledge-Based Image Analysis
12.5.1 Emulating Photointerpretation to Understand Knowledge Processing
12.5.2 The Structure of a Knowledge-Based Image Analysis System
12.5.3 Representing Knowledge in a Knowledge-Based Image Analysis System
12.5.4 Processing Knowledge: The Inference Engine
12.5.5 Rules as Justifiers of a Labelling Proposition
12.5.6 Endorsing a Labelling Proposition
12.5.7 An Example
12.6…Operational Multisource Analysis
12.7…Bibliography on Multisource Image Analysis
12.8…Problems
Appendix ASatellite Altitudes and Periods
Appendix BBinary Representation of DecimalNumbers
Appendix CEssential Results from Vector and MatrixAlgebra
Appendix DSome Fundamental Materialfrom Probability and Statistics
Appendix EPenalty Function Derivationof the Maximum Likelihood Decision Rule
Index
John A. Richards
Remote Sensing
Digital Image
Analysis
An Introduction
Fifth Edition
Remote Sensing Digital Image Analysis
John A. Richards
Remote Sensing Digital
Image Analysis
An Introduction
Fifth Edition
123
John A. Richards
ANU College of Engineering and
Computer Science
The Australian National University
Canberra, ACT
Australia
ISBN 978-3-642-30061-5
DOI 10.1007/978-3-642-30062-2
Springer Heidelberg New York Dordrecht London
ISBN 978-3-642-30062-2
(eBook)
Library of Congress Control Number: 2012938702
Ó Springer-Verlag Berlin Heidelberg 2013
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Preface
The first edition of this book appeared 25 years ago. Since then there have been
enormous advances in the availability of computing resources for the analysis of
remote sensing image data, and there are many more remote sensing programs and
sensors now in operation. There have also been significant developments in the
algorithms used for the processing and analysis of remote sensing imagery;
nevertheless, many of the fundamentals have substantially remained the same. It is
the purpose of this new edition to present material that has retained value since
those early days, along with new techniques that can be incorporated into an
operational framework for the analysis of remote sensing data.
This book is designed as a teaching text for the senior undergraduate and
postgraduate student, and as a fundamental treatment for those engaged in research
using digital image processing in remote sensing. The presentation level is for the
mathematical non-specialist. Since the very great number of operational users of
remote sensing come from the earth sciences communities, the text is pitched at a
level commensurate with their background. That is important because the recog-
nised authorities in the digital
image analysis literature tend to be from
engineering, computer science and mathematics. Although familiarity with a
certain level of mathematics and statistics cannot be avoided, the treatment here
works through analyses carefully, with a substantial degree of explanation, so that
those with a minimum of mathematical preparation may still draw benefit.
Appendices are included on some of the more important mathematical and
statistical concepts, but a familiarity with calculus is assumed.
From an operational point of view, it is important not to separate the techniques
and algorithms for image analysis from an understanding of remote sensing fun-
damentals. Domain knowledge guides the choice of data for analysis and allows
algorithms to be selected that best suit the task at hand. Such an operational
context is a hallmark of the treatment here. The coverage commences with a
summary of the sources and characteristics of image data, and the reflectance and
emission characteristics of earth surface materials, for those readers without a
detailed knowledge of the principles and practices of remote sensing. The book
v
vi
Preface
then progresses though image correction, image enhancement and image analysis,
so that digital data handling is properly located in its applications domain.
While incorporating new material, decisions have been taken to omit some
topics contained in earlier editions. In particular, the detailed compendium of
satellite programs and sensor characteristics, included in the body of the first three
editions and as an appendix in the fourth, has now been left out. There are two
reasons for that. First, new satellite and aircraft missions in optical and microwave
remote sensing are emerging more rapidly than the ability for a book such as this
to maintain currency and, notwithstanding this, all the material is now readily
obtainable through Internet sources. A detailed coverage of data compression in
remote sensing has also been left out.
Another change introduced with this edition relates to referencing conventions.
References are now included as footnotes rather than as end notes for each chapter,
as is more common in the scientific literature. This decision was taken to make the
tracking of references with the source citation simpler, and to allow the references
to be annotated and commented on when they appear in the text. Nevertheless,
each chapter concludes with a critical bibliography, again with comments, con-
taining the most important material in the literature for the topics treated in that
chapter. One of the implications of using footnotes is the introduction of the
standard terms ibid, which means the reference cited immediately before, and loc.
cit., which means cited previously among the most recent set of footnotes.
I am indebted to a number of people for the time, ideas and data they have
contributed to help bring this work to conclusion. My colleague and former
student, Dr Xiuping Jia, was a co-author of the third and fourth editions, a very
welcome contribution at the time when I was in management positions that left
insufficient time to carry out some of the detailed work required to create those
editions. On this occasion, Dr Jia’s own commitments have meant that she could
not participate in the project. I would like to place on record, however, my sincere
appreciation of her contributions to the previous editions that have flowed through
to this new version and to acknowledge the very many fruitful discussions we have
had on remote sensing image analysis research over the years of our collaboration.
Dr Terry Cocks, Managing Director of HyVista Corporation Pty Ltd, Australia,
very kindly made available HyMap hyperspectral imagery of Perth, Western
Australia to allow many of the examples contained in this edition to be generated.
Dr Larry Biehl of Purdue University was enormously patient and helpful in
bringing me up to an appropriate level of expertise with MultiSpec. That is a
valuable and user-friendly image analysis package that he and Professor David
Landgrebe have been steadily developing over the years. It is derived from the
original LARSYS system that was responsible for much digital image processing
research in remote sensing carried out during the 1960s and 1970s. Their trans-
ferring that system to personal computers has brought substantial and professional
processing capability within reach of any analyst and application specialist in
remote sensing.
Finally, it is with a great sense of gratitude that I acknowledge the generosity of
spirit of my wife Glenda for her support during the time it has taken to prepare this
Preface
vii
new edition, and for her continued and constant support of me right through my
academic career. At times, a writing task is relentless and those who contribute
most are friends and family, both through encouragement and taking time out of
family activities to allow the task to be brought to conclusion. I count myself very
fortunate indeed.
Canberra, ACT, Australia, February 2012
John A. Richards
Contents
1
2
Sources and Characteristics of Remote Sensing Image Data. . . . .
Energy Sources and Wavelength Ranges . . . . . . . . . . . . . . .
1.1
1.2
Primary Data Characteristics . . . . . . . . . . . . . . . . . . . . . . .
Remote Sensing Platforms . . . . . . . . . . . . . . . . . . . . . . . . .
1.3
What Earth Surface Properties are Measured? . . . . . . . . . . .
1.4
1.4.1
Sensing in the Visible and Reflected
Infrared Ranges . . . . . . . . . . . . . . . . . . . . . . . . . .
Sensing in the Thermal Infrared Range . . . . . . . . . .
Sensing in the Microwave Range . . . . . . . . . . . . . .
1.4.2
1.4.3
Spatial Data Sources in General and Geographic
Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Scale in Digital Image Data . . . . . . . . . . . . . . . . . . . . . . . .
Digital Earth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
How This Book is Arranged . . . . . . . . . . . . . . . . . . . . . . .
Bibliography on Sources and Characteristics
of Remote Sensing Image Data . . . . . . . . . . . . . . . . . . . . .
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5
1.6
1.7
1.8
1.9
1.10
Correcting and Registering Images . . . . . . . . . . . . . . . . . . . . . . .
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sources of Radiometric Distortion . . . . . . . . . . . . . . . . . . .
2.2
Instrumentation Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3
Sources of Distortion. . . . . . . . . . . . . . . . . . . . . . .
2.3.1
2.3.2
Correcting Instrumentation Errors . . . . . . . . . . . . . .
Effect of the Solar Radiation Curve and the
Atmosphere on Radiometry . . . . . . . . . . . . . . . . . . . . . . . .
Compensating for the Solar Radiation Curve . . . . . . . . . . . .
Influence of the Atmosphere . . . . . . . . . . . . . . . . . . . . . . .
Effect of the Atmosphere on Remote Sensing Imagery . . . . .
2.5
2.6
2.7
2.4
1
1
4
6
10
12
13
15
18
19
20
21
23
25
27
27
28
28
28
30
31
32
33
37
ix
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