Remote sensing digital image analysis an introduction
Contents: Sources and Characteristics of Remote Sensing Image Data.- Error Correction and Registration of Image Data.- The Interpretation of Digital Image Data.- Radiometric Enhancement Techniques.- Geometric Enhancement Using Image Domain Techniques.- Multispectral Transformations of Image Data.- F...
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| 100 | 1 | |a Richards, John Alan |d 1945- |e Verfasser |0 (DE-588)120671549 |4 aut | |
| 245 | 1 | 0 | |a Remote sensing digital image analysis |b an introduction |c John A. Richards ; Xiuping Jia |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2006 | |
| 300 | |a XXV, 439 S. |b Ill., graph. Darst., Kt. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | |a Contents: Sources and Characteristics of Remote Sensing Image Data.- Error Correction and Registration of Image Data.- The Interpretation of Digital Image Data.- Radiometric Enhancement Techniques.- Geometric Enhancement Using Image Domain Techniques.- Multispectral Transformations of Image Data.- Fourier Transformation of Image Data.- Supervised Classification Techniques.- Clustering and Unsupervised Classification.- Feature Reduction.- Image Classification Methodologies.- Data Fusion.- Interpretation of Hyperspectral Image Data.- Appendices | ||
| 650 | 4 | |a Image processing |x Digital techniques | |
| 650 | 4 | |a Remote sensing | |
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Datensatz im Suchindex
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| _version_ | 1816712509700177920 |
| adam_text | Contents
Sources
and Characteristics of Remote Sensing Image Data
........ 1
1.1
Introduction to Data Sources
................................. 1
1.1.1
Characteristics of Digital Image Data
.................. 1
1.1.2
Spectral Ranges Commonly Used in Remote Sensing
.... 4
1.1.3
Concluding Remarks
................................ 8
1.2
Remote Sensing Platforms
.................................. 9
1.3
Image Data Sources in the Microwave Region
.................. 12
1.3.1
Side Looking Airborne Radar
and Synthetic Aperture Radar
........................ 12
1.4
Spatial Data Sources in General
.............................. 15
1.4.1
Types of Spatial Data
............................... 15
1.4.2
Data Formats
...................................... 17
1.4.3
Geographic Information Systems
(GIS)
................ 18
1.4.4
The Challenge to Image Processing and Analysis
........ 20
1.5
A Comparison of Scales in Digital Image Data
.................. 21
References for Chapter
1......................................... 22
Problems
...................................................... 23
Error Correction and Registration of Image Data
................ 27
2.1
Sources of
Radiometrie
Distortion
............................ 27
2.1.1
The Effect of the Atmosphere on Radiation
............. 28
2.1.2
Atmospheric Effects on Remote Sensing Imagery
........ 31
2.1.3
Instrumentation Errors
.............................. 31
2.2
Correction of
Radiometrie
Distortion
.......................... 32
2.2.1
Detailed Correction of Atmospheric Effects
............. 33
2.2.2
Bulk Correction of Atmospheric Effects
................ 34
2.2.3
Correction of Instrumentation Errors
.................. 36
2.3
Sources of Geometric Distortion
............................. 37
2.3.1
Earth Rotation Effects
............................... 38
2.3.2
Panoramic Distortion
............................... 39
2.3.3
Earth Curvature
.................................... 42
2.3.4
Scan Time Skew
................................... 43
2.3.5
Variations in Platform Altitude, Velocity and Attitude
..... 43
2.3.6
Aspect Ratio Distortion
.............................. 44
2.3.7
Sensor Scan Nonlinearities
........................... 45
2.4
Correction of Geometric Distortion
........................... 46
2.4.1
Use of Mapping Polynomials for Image Correction
...... 46
2.4.1.1
Mapping Polynomials
and Ground Control Points
................... 47
2.4.1.2
Resampling
............................... 48
2.4.1.3
Interpolation
............................... 48
2.4.1.4
Choice of Control Points
.................... 51
2.4.1.5
Example of Registration to a Map Grid
........ 51
2.4.2
Mathematical Modelling
............................. 54
2.4.2.1
Aspect Ratio Correction
..................... 54
2.4.2.2
Earth Rotation Skew Correction
.............. 55
2.4.2.3
Image Orientation to North-South
............. 55
2.4.2.4
Correction of Panoramic Effects
.............. 55
2.4.2.5
Combining the Corrections
.................. 56
2.5
Image Registration
......................................... 56
2.5.1
Georeferencing and Geocoding
....................... 56
2.5.2
Image to Image Registration
......................... 57
2.5.3
Control Point Localisation by Correlation
.............. 57
2.5.4
Example of Image to Image Registration
............... 58
2.6
Miscellaneous Image Geometry Operations
.................... 59
2.6.1
Image Rotation
.................................... 61
2.6.2
Scale Changing and Zooming
........................ 61
References for Chapter
2......................................... 61
Problems
...................................................... 62
The Interpretation of Digital Image Data
....................... 67
3.1
Approaches to Interpretation
................................. 67
3.2
Forms of Imagery for
Photointerpretation...................... 69
3.3
Computer Processing for
Photointerpretation ................... 72
3.4
An Introduction to Quantitative Analysis
-
Classification
......... 72
3.5
Multispectral Space and Spectral Classes
...................... 75
3.6
Quantitative Analysis by Pattern Recognition
................... 77
3.6.1
Pixel Vectors and Labelling
.......................... 77
3.6.2
Unsupervised Classification
.......................... 78
3.6.3
Supervised Classification
............................ 78
References for Chapter
3......................................... 80
Problems
...................................................... 81
Radiometrie
Enhancement Techniques
......................... 83
4.1
Introduction
............................................... 83
4.1.1
Point
Operations and Look Up Tables
.................. 83
4.1.2
Scalar and Vector Images
............................. 83
4.2
The Image Histogram
....................................... 84
4.3
Contrast Modification in Image Data
.......................... 84
4.3.1
Histogram Modification Rule
......................... 84
4.3.2
Linear Contrast Modification
......................... 86
4.3.3
Saturating Linear Contrast Enhancement
................ 88
4.3.4
Automatic Contrast Enhancement
...................... 88
4.3.5
Logarithmic and Exponential Contrast Enhancement
...... 89
4.3.6
Piecewise Linear Contrast Modification
................ 89
4.4
Histogram Equalization
..................................... 90
4.4.1
Use of the Cumulative Histogram
...................... 90
4.4.2
Anomalies in Histogram Equalization
.................. 95
4.5
Histogram Matching
........................................ 97
4.5.1
Principle of Histogram Matching
...................... 97
4.5.2
Image to Image Contrast Matching
.................... 98
4.5.3
Matching to a Mathematical Reference
................. 99
4.6
Density Slicing
............................................101
4.6.1
Black and White Density Slicing
......................101
4.6.2
Colour Density Slicing and Pseudocolouring
............104
References for Chapter
4.........................................104
Problems
......................................................105
Geometric Enhancement Using Image Domain Techniques
........ 109
5.1
Neighbourhood Operations
..................................109
5.2
Template Operators
.........................................109
5.3
Geometric Enhancement as a Convolution Operation
.............110
5.4
Image Domain Versus Fourier Transformation Approaches
........113
5.5
Image Smoothing (Low Pass Filtering)
.........................115
5.5.1
Mean Value Smoothing
..............................115
5.5.2
Median Filtering
....................................116
5.6
Edge Detection and Enhancement
.............................118
5.6.1
Linear Edge Detecting Templates
......................120
5.6.2
Spatial Derivative Techniques
.........................121
5.6.2.1
The Roberts Operator
........................121
5.6.2.2
The
Sobel
Operator
.........................122
5.6.2.3
The Prewitt Operator
........................122
5.6.3
Thinning, Linking and Border Responses
...............123
5.6.4
Edge Enhancement by Subtractive Smoothing
(Sharpening)
.......................................123
5.7
Line Detection
.............................................125
5.7.1
Linear Line Detecting Templates
......................125
5.7.2
Non-linear and Semi-linear Line Detecting Templates
.....125
5.8
General Convolution Filtering
................................127
5.9
Detecting Geometric Properties
..............................128
5.9.1
Texture
...........................................128
5.9.2
Spatial
Correlation - The Semivariogram
...............131
5.9.3
Shape Detection
....................................132
References for Chapter
5.........................................132
Problems
......................................................134
Multispectral Transformations of Image Data
................... 137
6.1
The Principal Components Transformation
.....................137
6.1.1
The Mean Vector and Covariance Matrix
................138
6.1.2
A Zero Correlation, Rotational Transform
...............141
6.1.3
Examples
-
Some Practical Considerations
..............145
6.1.4
The Effect of an Origin Shift
..........................150
6.1.5
Application of Principal Components
in Image Enhancement and Display
....................150
6.1.6
The Taylor Method of Contrast Enhancement
............151
6.1.7
Other Applications of Principal Components Analysis
.... 154
6.2
Noise Adjusted Principal Components Transformation
...........154
6.3
The
Kauth-Thomas Tasseled
Cap Transformation
................156
6.4
Image Arithmetic, Band Ratios and Vegetation Indices
...........160
References for Chapter
6.........................................161
Problems
......................................................162
Fourier Transformation of Image Data
......................... 165
7.1
Introduction
...............................................165
7.2
Special Functions
..........................................165
7.2.1
The Complex Exponential Function
....................166
7.2.2
The Dirac Delta Function
............................166
7.2.2.1
Properties of the Delta Function
...............167
7.2.3
The Heaviside Step Function
..........................168
7.3
Fourier Series
..............................................168
7.4
The Fourier Transform
......................................169
7.5
Convolution
...............................................171
7.5.1
The Convolution Integral
.............................171
7.5.2
Convolution with an Impulse
.........................171
7.5.3
The Convolution Theorem
............................173
7.6
Sampling Theory
...........................................173
7.7
The Discrete Fourier Transform
...............................176
7.7.1
The Discrete Spectrum
...............................176
7.7.2
Discrete Fourier Transform Formulae
..................177
7.7.3
Properties of the Discrete Fourier Transform
............178
7.7.4
Computation of the Discrete Fourier Transform
..........179
7.7.5
Development of the Fast Fourier Transform Algorithm
.... 179
7.7.6
Computational Cost of the Fast Fourier Transform
........183
7.7.7
Bit Shuffling and Storage Considerations
...............184
7.8
The Discrete Fourier Transform of an Image
....................184
7.8.1 Definition .........................................184
7.8.2 Evaluation
of the Two Dimensional, Discrete Fourier
Transform
.........................................185
7.8.3
The Concept of Spatial Frequency
.....................185
7.8.4
Image Filtering for Geometric Enhancement
............187
7.8.5
Convolution in Two Dimensions
.......................188
7.9
Concluding Remarks
........................................189
References for Chapter
7.........................................191
Problems
......................................................191
Supervised Classification Techniques
.......................... 193
8.1
Steps in Supervised Classification
............................193
8.2
Maximum Likelihood Classification
..........................194
8.2.1
Bayes
Classification
................................194
8.2.2
The Maximum Likelihood Decision Rule
...............195
8.2.3
Multivariate Normal Class Models
....................196
8.2.4
Decision Surfaces
..................................196
8.2.5
Thresholds
........................................197
8.2.6
Number of Training Pixels Required for Each Class
......199
8.2.7
A Simple Illustration
................................199
8.3
Minimum Distance Classification
............................201
8.3.1
The Case of Limited Training Data
....................201
8.3.2
The Discriminant Function
...........................202
8.3.3
Degeneration of Maximum Likelihood
to Minimum Distance Classification
...................203
8.3.4
Decision Surfaces
..................................204
8.3.5
Thresholds
........................................204
8.4
Parallelepiped Classification
.................................204
8.5
Classification Time Comparison of the Classifiers
...............206
8.6
Other Supervised Approaches
................................206
8.6.1
The Mahalanobis Classifier
..........................206
8.6.2
Table Look Up Classification
.........................207
8.6.3
The kNN (Nearest Neighbour) Classifier
...............207
8.7
Gaussian Mixture Models
...................................208
8.8
Context Classification
......................................209
8.8.1
The Concept of Spatial Context
.......................209
8.8.2
Context Classification by Image Pre-processing
.........210
8.8.3
Post Classification Filtering
..........................211
8.8.4
Probabilistic Label Relaxation
........................211
8.8.4.1
The Basic Algorithm
........................211
8.8.4.2
The Neighbourhood Function
................212
8.8.4.3
Determining the Compatibility Coefficients
.....213
8.8.4.4
The Final Step
-
Stopping the Process
.........214
8.8.4.5
Examples
.................................215
8.8.5
Handling Spatial Context by Markov Random Fields
.....216
8.9
Non-parametric Classification: Geometric Approaches
...........219
8.9.1
Linear Discrimination
...............................220
. 8.9.1.1
Concept of a Weight Vector
..................220
8.9.1.2
Testing Class Membership
...................221
8.9.1.3
Training
..................................221
8.9.1.4
Setting the Correction Increment
..............223
8.9.1.5
Classification
-
The Threshold Logic Unit
......224
8.9.1.6
Multicategory Classification
.................225
8.9.2
Support Vector Classifiers
.............................226
8.9.2.1
Linearly Separable Data
.....................226
8.9.2.2
Linear Inseparability
-
The Use of Kernel Functions
.................230
8.9.2.3
Multicategory Classification
.................231
8.9.3
Networks of Classifiers
-
Solutions of Nonlinear Problems
231
8.9.4
The Neural Network Approach
.......................232
8.9.4.1
The Processing Element
.....................232
8.9.4.2
Training the Neural Network
-
Backpropagation
...........................234
8.9.4.3
Choosing the Network Parameters
............238
8.9.4.4
Examples
.................................238
References for Chapter
8.........................................243
Problems
......................................................246
Clustering and Unsupervised Classification
..................... 249
9.1
Delineation of Spectral Classes
..............................249
9.2
Similarity Metrics and Clustering Criteria
......................249
9.3
The Iterative Optimization (Migrating Means)
Clustering Algorithm
.......................................251
9.3.1
The Basic Algorithm
................................252
9.3.2
Mergings and Deletions
.............................252
9.3.3
Splitting Elongated Clusters
..........................254
9.3.4
Choice of Initial Cluster Centres
......................254
9.3.5
Clustering Cost
....................................254
9.4
Unsupervised Classification and Cluster Maps
..................255
9.5
A Clustering Example
......................................255
9.6
A Single Pass Clustering Technique
...........................257
9.6.1
Single Pass Algorithm
...............................257
9.6.2
Advantages and Limitations
..........................259
9.6.3
Strip Generation Parameter
..........................259
9.6.4
Variations on the Single Pass Algorithm
................259
9.6.5
An Example
.......................................260
9.7
Agglomerative Hierarchical Clustering
........................260
9.8
Clustering by Histogram Peak Selection
....................... 263
References for Chapter
9.........................................264
Problems
......................................................265
10
Feature
Reduction
........................................... 267
10.1
Feature
Reduction and Separability
...........................267
10.2
Separability Measures
for Multivariate Normal Spectral Class Models
.................268
10.2.1
Distribution Overlaps
...............................268
10.2.2
Divergence
........................................269
10.2.2.1
A General Expression
.......................269
10.2.2.2
Divergence of a Pair of Normal Distributions
... 270
10.2.2.3
Use of Divergence for Feature Selection
.......271
10.2.2.4
A Problem with Divergence
..................272
10.2.3
The Jeffries-Matusita (JM) Distance
...................273
10.2.3.1
Definition
.................................273
10.2.3.2
Comparison of Divergence and JM Distance
.... 274
10.2.4
Transformed Divergence
.............................274
10.2.4.1
Definition
.................................274
10.2.4.2
Relation Between Transformed Divergence
and Probability of Correct Classification
.......275
10.2.4.3
Use of Transformed Divergence in Clustering
... 276
10.3
Separability Measures for Minimum Distance Classification
......276
10.4
Feature Reduction by Data Transformation
.....................276
10.4.1
Feature Reduction
Using the Principal Components Transformation
........277
10.4.2
Canonical Analysis as a Feature Selection Procedure
.....279
10.4.2.1
Within Class
and Among Class Covariance Matrices
.........280
10.4.2.2
A Separability Measure
......................281
10.4.2.3
The Generalised Eigenvalue Equation
..........281
10.4.2.4
An Example
...............................283
10.4.3
Discriminant Analysis Feature Extraction (DAFE)
.......285
10.4.4
Non-parametric Discriminant Analysis
and Decision Boundary Feature Extraction (DBFE)
......286
10.4.5
Non-parametric Weighted Feature Extraction (NWFE)
... 290
10.4.6
Arithmetic Transformations
..........................292
References for Chapter
10........................................292
Problems
......................................................293
11
Image Classification Methodologies
............................ 295
11.1
Introduction
...............................................295
11.2
Supervised Classification
...................................295
11.2.1
Outline
...........................................295
11.2.2
Determination of Training Data
.......................296
11.2.3
Feature Selection
...................................297
11.2.4
Detecting
Multimodal
Distributions
...................297
11.2.5
Presentation of Results
..............................298
11.2.6
Effect of Resampling on Classification
.................298
11.3
Unsupervised Classification
.................................299
11.3.1
Outline, and Comparison with Supervised Methods
......299
11.3.2
Feature Selection
...................................301
11.4
A Hybrid Supervised/Unsupervised Methodology
...............301
11.4.1
The Essential Steps
.................................301
11.4.2
Choice of the Clustering Regions
.....................302
11.4.3
Rationalisation of the Number of Spectral Classes
.......302
11.5
Assessment of Classification Accuracy
........................303
11.5.1
Using a Testing Set of Pixels
.........................303
11.5.2
The Leave One Out Method of Accuracy Assessment
-
Cross Validation
....................................307
11.6
Case Study
1:
Irrigated Area Determination
....................307
11.6.1
Background
.......................................308
11.6.2
The Study Region
...................................308
11.6.3
Clustering
.........................................309
11.6.4
Signature Generation
................................312
11.6.5
Classification and Results
............................312
11.6.6
Concluding Remarks
................................312
11.7
Case Study
2: Multitemporal
Monitoring of Bush Fires
..........314
11.7.1
Background
.......................................314
11.7.2
Simple Illustration of the Technique
...................314
11.7.3
The Study Area
....................................316
11.7.4
Registration
.......................................316
11.7.5
Principal Components Transformation
.................317
11.7.6
Classification of Principal Components Imagery
.........319
11.8
Hierarchical Classification
..................................321
11.8.1
The Decision Tree Classifier
.........................321
11.8.2
Decision Tree Design
...............................323
11.8.3
Progressive Two-Class Decision Classifier
..............324
11.8.4
Error Accumulation in a Decision Tree
.................327
11.9
A Note on Hyperspectral Data Classification
...................328
References for Chapter
11........................................329
Problems
......................................................331
12
Multisource, Multisensor Methods
............................. 333
12.1
The Stacked Vector Approach
................................334
12.2
Statistical Multisource Methods
..............................334
12.2.1
Joint Statistical Decision Rules
.......................334
12.2.2
Committee Classifiers
...............................335
12.2.3
Opinion Pools and Consensus Theoretic Methods
........336
12.2.4
Use of Prior Probability
.............................337
12.2.5
Supervised Label Relaxation
.........................337
12.3
The Theory of Evidence
....................................338
12.3.1
The Concept of Evidential Mass
......................338
12.3.2
Combining Evidence
-
the Orthogonal Sum
............340
12.3.3
Decision Rule
......................................341
12.4
Knowledge-Based Image Analysis
............................342
12.4.1
Knowledge Processing: Emulating
Photointerpretation ... 342
12.4.2
Fundamentals of a Knowledge-Based
Image Analysis System
..............................344
12.4.2.1
Structure
..................................344
12.4.2.2
Representation of Knowledge: Rules
..........345
12.4.2.3
The Inference Mechanism
...................346
12.4.3
Handling Multisource and Multisensor Data
............347
12.4.4
An Example
.......................................349
12.4.4.1
Rules as Justifiers for a Labelling Proposition
... 350
12.4.4.2
Endorsement of a Labelling Proposition
........351
12.4.4.3
Knowledge Base and Results
.................352
References for Chapter
12........................................354
Problems
......................................................356
13
Interpretation of Hyperspectral Image Data
..................... 359
13.1
Data Characteristics
........................................359
13.2
The Challenge to Interpretation
..............................361
13.2.1
Data Volume
.......................................362
13.2.2
Redundancy
.......................................362
13.2.3
The Need for Calibration
............................364
13.2.4
The Problem of Dimensionality:
The Hughes Phenomenon
............................364
13.3
Data Calibration Techniques
.................................366
13.3.1
Detailed
Radiometrie
Correction
......................366
13.3.2
Data Normalisation
.................................367
13.3.3
Approximate
Radiometrie
Correction
..................368
13.4
Interpretation Using Spectral Information
......................368
13.4.1
Spectral Angle Mapping
.............................368
13.4.2
Using Expert Spectral Knowledge
and Library Searching
...............................369
13.4.3
Library Searching by Spectral Coding
.................371
13.4.3.1
Binary Spectral Codes
......................371
13.4.3.2
Matching Algorithms
.......................373
13.5
Hyperspectral Interpretation by Statistical Methods
..............373
13.5.1
Limitations of Traditional
Thematic Mapping Procedures
.......................373
13.5.2
Block-based Maximum Likelihood Classification
........375
13.6
Feature Reduction
.........................................377
13.6.1
Feature Selection
...................................378
13.6.2
Spectral Transformations
............................379
13.6.3
Feature Selection from Principal Components
Transformed Data
..................................381
13.7
Regularised Covariance Estimators
...........................381
13.8
Compression
of Hyperspectral Data
...........................382
13.9
Spectral
Unmixing: End Member Analysis .....................
385
References for Chapter
13........................................386
Problems
......................................................387
Missions and Sensors
........................................ 389
A.I Weather Satellite Sensors
...................................389
A.
1.1
Polar Orbiting and Geosynchronous Satellites
...........389
A.
1.2
The NOAA AVHRR
(Advanced Very High Resolution Radiometer)
..........390
A.
1.3
The Nimbus CZCS (Coastal Zone Colour Scanner)
......390
A.
1.4
GMS VISSR (Visible and Infrared Spin Scan Radiometer)
and GOES Imager
..................................391
A.2 Earth Resource Satellite Sensors
in the Visible and Infrared Regions
...........................391
A.2.1 The Landsat System
................................391
A.2.2 The Landsat Instrument Complement
..................393
A.2.3 The Return Beam Vidicon (RBV)
.....................393
A.2.4 The Multispectral Scanner
(MSS)
.....................394
A.2.5 The Thematic Mapper (TM)
and Enhanced Thematic Mapper
+
(ETM+)
.............396
A.2.6 The SPOT HRV, HRVIR, HRG, HRS and Vegetation
Instruments
........................................397
A.2.7 ADEOS (Advanced Earth Observing Satellite)
..........398
A.2.8 Sea-Viewing Wide Field of View Sensor (SeaWiFS)
......399
A.2.9 Marine Observation Satellite
(MOS)
...................400
A.2.10 Indian Remote Sensing Satellite
(1RS) .................401
A.2.
11
RESURS-Ol
......................................401
A.2.
12
The Earth Observing
1
(EO-1) Mission
................401
A.2.13 Aqua and Terra
.....................................401
A.2.
14
Ikonos
............................................405
A.3 Aircraft Scanners in the Visible and Infrared Regions
............405
A.3.1 General Considerations
..............................405
A.3.
2
Airborne Imaging Spectrometers
......................406
A.4
Spaceborne
Imaging Radar Systems
..........................407
A.4.1 The Seasat
SAR....................................407
A.4.2
Spaceborne
(Shuttle) Imaging
Radar-A
(SIR
-А)
.........
407
A.4.3
Spaceborne
(Shuttle) Imaging
Radar-B (SIR-B)
.........409
A.4.4
Spaceborne
(Shuttle) Imaging
Radar-C
(SIR-C)/X-Band
Synthetic Aperture Radar (X-SAR)
....................409
A.4.5
ERS-
1,2 ..........................................409
A.4.6 JERS-l
...........................................410
A.4.7 Radarsat
..........................................410
A.4.8 Shuttle Radar Topography Mission (SRTM)
............410
A.4.9 Envisat Advanced Synthetic Aperture Radar
(ASAR)
.....411
A4.10
The Advanced Land Observing Satellite
(ALOS)
PÄLSAR
..................................411
A.5 Aircraft Imaging Radar Systems
..............................411
References for Appendix A
.......................................412
В
Satellite Altitudes and Periods
................................. 413
References for Appendix
В
.......................................414
С
Binary Representation of Decimal Numbers
..................... 415
D
Essential Results from Vector and Matrix Algebra
................ 417
D.I Definition of a Vector and a Matrix
...........................417
D.2 Properties of Matrices
......................................419
D.3 Multiplication, Addition and Subtraction of Matrices
............420
D.4 The Eigenvalues and Eigenvectors of a Matrix
..................420
D.5 Some Important Matrix, Vector Operations
.....................421
D.6 An Orthogonal Matrix
-
The Concept of Matrix Transpose
.......421
D.7 Diagonalisation of a Matrix
..................................422
E Some
Fundamental Material from Probability and Statistics
....... 423
E.I Conditional Probability
.....................................423
E.2 The Normal Probability Distribution
..........................424
E.2.1 The Univariate Case
................................424
E.2.2 The Multivariate Case
...............................425
References for Appendix
E
.......................................425
F
Penalty Function Derivation
of the Maximum Likelihood Decision Rule
...................... 427
F.I Loss Functions and Conditional Average Loss
..................427
F.2 A Particular Loss Function
..................................428
References for Appendix
F
.......................................429
Subject Index
................................................... 431
|
| any_adam_object | 1 |
| author | Richards, John Alan 1945- Jia, Xiuping |
| author_GND | (DE-588)120671549 |
| author_facet | Richards, John Alan 1945- Jia, Xiuping |
| author_role | aut aut |
| author_sort | Richards, John Alan 1945- |
| author_variant | j a r ja jar x j xj |
| building | Verbundindex |
| bvnumber | BV020032122 |
| classification_rvk | RB 10232 |
| classification_tum | DAT 760f BAU 967f |
| ctrlnum | (OCoLC)181444117 (DE-599)BVBBV020032122 |
| discipline | Informatik Bauingenieurwesen Vermessungswesen Geographie |
| edition | 4. ed. |
| format | Book |
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| genre_facet | Einführung |
| id | DE-604.BV020032122 |
| illustrated | Illustrated |
| indexdate | 2024-11-25T17:26:05Z |
| institution | BVB |
| isbn | 3540251286 9783540251286 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013353323 |
| oclc_num | 181444117 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-29T DE-29 DE-20 DE-703 DE-739 DE-91 DE-BY-TUM DE-83 DE-11 DE-188 DE-B768 |
| owner_facet | DE-91G DE-BY-TUM DE-29T DE-29 DE-20 DE-703 DE-739 DE-91 DE-BY-TUM DE-83 DE-11 DE-188 DE-B768 |
| physical | XXV, 439 S. Ill., graph. Darst., Kt. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Richards, John Alan 1945- Jia, Xiuping Remote sensing digital image analysis an introduction Image processing Digital techniques Remote sensing Fernerkundung (DE-588)4016796-3 gnd Bildverarbeitung (DE-588)4006684-8 gnd Datenverarbeitung (DE-588)4011152-0 gnd |
| subject_GND | (DE-588)4016796-3 (DE-588)4006684-8 (DE-588)4011152-0 (DE-588)4151278-9 |
| title | Remote sensing digital image analysis an introduction |
| title_auth | Remote sensing digital image analysis an introduction |
| title_exact_search | Remote sensing digital image analysis an introduction |
| title_full | Remote sensing digital image analysis an introduction John A. Richards ; Xiuping Jia |
| title_fullStr | Remote sensing digital image analysis an introduction John A. Richards ; Xiuping Jia |
| title_full_unstemmed | Remote sensing digital image analysis an introduction John A. Richards ; Xiuping Jia |
| title_short | Remote sensing digital image analysis |
| title_sort | remote sensing digital image analysis an introduction |
| title_sub | an introduction |
| topic | Image processing Digital techniques Remote sensing Fernerkundung (DE-588)4016796-3 gnd Bildverarbeitung (DE-588)4006684-8 gnd Datenverarbeitung (DE-588)4011152-0 gnd |
| topic_facet | Image processing Digital techniques Remote sensing Fernerkundung Bildverarbeitung Datenverarbeitung Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013353323&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT richardsjohnalan remotesensingdigitalimageanalysisanintroduction AT jiaxiuping remotesensingdigitalimageanalysisanintroduction |