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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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Bibliographische Detailangaben
Beteiligte Personen:	Richards, John Alan 1945- (VerfasserIn), Jia, Xiuping (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Berlin [u.a.] Springer 2006
Ausgabe:	4. ed.
Schlagwörter:	Image processing > Digital techniques Remote sensing Fernerkundung Bildverarbeitung Datenverarbeitung Einführung
Links:	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
Zusammenfassung:	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
Umfang:	XXV, 439 S. Ill., graph. Darst., Kt.
ISBN:	3540251286 9783540251286

Internformat

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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
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Richards ; Xiuping Jia</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">4. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin [u.a.]</subfield><subfield code="b">Springer</subfield><subfield code="c">2006</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXV, 439 S.</subfield><subfield code="b">Ill., graph. 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genre	1\p (DE-588)4151278-9 Einführung gnd-content
genre_facet	Einführung
id	DE-604.BV020032122
illustrated	Illustrated
indexdate	2024-12-20T12:10:36Z
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

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