Úplné zobrazení záznamu

Toto je statický export z katalogu ze dne 19.12.2020. Zobrazit aktuální podobu v katalogu.

Bibliografická citace

.
0 (hodnocen0 x )
(1) Půjčeno:1x 
BK
4th ed.
Berlin : Springer, c2006
xxv,439 s. : il.

objednat
ISBN 3-540-25128-6 (váz.)
Obsahuje ilustrace, tabulky, bibliografické odkazy, předmluvy, jmenný rejstřík
Snímky kosmické - zpracování digitální - učebnice vysokošk.
Země - průzkum dálkový - učebnice vysokošk.
000092034
Contents // 1 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 // 2 Error Correction and Registration of Image Data... 27 // 2.1 Sources of Radiometric 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 Radiometric 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 // XVI Contents // 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 // 3 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 // 4 Radiometric Enhancement Techniques... 83 // 4.1 Introduction... 83 // Contents XVII // 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 // 5 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 // XVIII Contents // 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 // 6 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 // 7 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 // Contents XIX // 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 // XX // Contents // 8.9 Non-parametric Classification: Geometrie 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 // 5.9.4.4 Examples ...238 // References for Chapter 8...243 // Problems...246 // 9 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 // Contents XXI // 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 // XXII Contents // 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 // Contents XXIII // 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 Radiometric Correction...366 // 13.3.2 Data Normalisation ...367 // 13.3.3 Approximate Radiometric 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 // XXIV Contents // 13.8 Compression of Hyperspectral Data...382 // 13.9 Spectral Unmixing: End Member Analysis ...385 // References for Chapter 13...386 // Problems...387 // A Missions and Sensors... 389 // A.l 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 (IRS) ...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 Spacebome Imaging Radar Systems ...407 // A.4.1 The Seasat SAR...407 // A.4.2 Spacebome (Shuttle) Imaging Radar-? (SIR-A) ...407 // A.4.3 Spacebome (Shuttle) Imaging Radar-? (SIR-B) ...409 // A.4.4 Spacebome (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-1 ...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 // Contents XXV // A.4 ? 0 The Advanced Land Observing Satellite // (ALOS) FALSAR ...411 // A.5 Aircraft Imaging Radar Systems...411 // References for Appendix A...412 // ? Satellite Altitudes and Periods... 413 // References for Appendix ?...414 // C Binary Representation of Decimal Numbers... 415 // D Essential Results from Vector and Matrix Algebra... 417 // D.l 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. 1 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. 1 Loss Functions and Conditional Average Loss ...427 // F.2 A Particular Loss Function ...428 // References for Appendix F...429 // Subject Index... 431

Zvolte formát: Standardní formát Katalogizační záznam Zkrácený záznam S textovými návěštími S kódy polí MARC