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Remote Sensing Digital Image Analysis [[electronic resource] ] : An Introduction / / by John A. Richards
| Remote Sensing Digital Image Analysis [[electronic resource] ] : An Introduction / / by John A. Richards |
| Autore | Richards John A |
| Edizione | [5th ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (502 p.) |
| Disciplina | 621.3678 |
| Soggetto topico |
Signal processing
Image processing Speech processing systems Geotechnical engineering Remote sensing Optical data processing Ecotoxicology Signal, Image and Speech Processing Geotechnical Engineering & Applied Earth Sciences Remote Sensing/Photogrammetry Image Processing and Computer Vision |
| ISBN |
3-642-30062-6
978-3-642-30062-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Sources and characteristics of remote sensing image data -- correcting and registering images -- interpreting images -- radiometric enhancement of images -- geometric processing and enhancement: image domain techniques -- spectral domain image transforms -- spatial domain image transforms -- supervised classification techniques -- clustering and unsupervised classification -- Feature Reduction -- Image Classification in Practice -- Multisource Image Analysis. |
| Record Nr. | UNINA-9910437767903321 |
Richards John A
|
||
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Remote Sensing Digital Image Analysis [[electronic resource] ] : An Introduction / / by John A. Richards
| Remote Sensing Digital Image Analysis [[electronic resource] ] : An Introduction / / by John A. Richards |
| Autore | Richards John A |
| Edizione | [2nd ed. 1993.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1993 |
| Descrizione fisica | 1 online resource (XX, 340 p. 160 illus., 14 illus. in color.) |
| Disciplina | 621.36/78 |
| Soggetto topico |
Geographical information systems
Waste management Water pollution Air pollution Soil science Soil conservation Noise control Geographical Information Systems/Cartography Waste Management/Waste Technology Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Soil Science & Conservation Noise Control |
| ISBN | 3-642-88087-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 — Sources and Characteristics of Remote Sensing Image Data -- 1.1 Introduction to Data Sources -- 1.1.1 Characteristics of Digital Image Data -- 1.1.2 Spectral Ranges Commonly Used in Remote Sensing -- 1.1.3 Concluding Remarks -- 1.2 Weather Satellite Sensors -- 1.2.1 Polar Orbiting and Geosynchronous Satellites -- 1.2.2 The NOAA AVHRR (Advanced Very High Resolution Radiometer) -- 1.2.3 The Nimbus CZCS (Coastal Zone Colour Scanner) -- 1.2.4 GMS VISSR (Visible and Infrared Spin Scan Radiometer) -- 1.3 Earth Resource Satellite Sensors in the Visible and Infrared Regions -- 1.3.1 The Landsat System -- 1.3.2 The Landsat Instrument Complement -- 1.3.3 The Return Beam Vidicon(RBV) -- 1.3.4 The Multispectral Scanner (MSS) -- 1.3.5 The Thematic Mapper (TM) -- 1.3.6 The SPOT High Resolution Visible (HRV) Imaging Instrument -- 1.3.7 The Skylab S 192 Multispectral Scanner -- 1.3.8 The Heat Capacity Mapping Radiometer (HCMR) -- 1.3.9 Marine Observation Satellite (MOS) -- 1.3.10 Indian Remote Sensing Satellite (IRS) -- 1.4 Aircraft Scanners in the Visible and Infrared Regions -- 1.4.1 General Considerations -- 1.4.2 The Daedalus AADS 1240/1260 Multispectral Line Scanner -- 1.4.3 The Airborne Thematic Mapper (ATM) -- 1.4.4 The Thermal Infrared Multispectral Scanner (TIMS) -- 1.4.5 The MDA MEIS-II Linear Array Aircraft Scanner -- 1.4.6 Imaging Spectrometers -- 1.5 Image Data Sources in the Microwave Region -- 1.5.1 Side Looking Airborne Radar and Synthetic Aperture Radar -- 1.5.2 TheSeasatSAR -- 1.5.3 Shuttle Imaging Radar-A (SIR-A) -- 1.5.4 Shuttle Imaging Radar-B(SIR-B) -- 1.5.5 ERS-1 -- 1.5.6 JERS-1 -- 1.5.7 Radarsat -- 1.5.8 Aircraft Imaging Radar Systems -- 1.6 Spatial Data Sources in General -- 1.6.1 Types of Spatial Data -- 1.6.2 Data Formats -- 1.6.3 Geographic Information Systems (GIS) -- 1.6.4 The Challenge to Image Processing and Analysis -- 1.7 A Comparison of Scales in Digital Image Data -- References for Chapter 1 -- Problems -- 2 — Error Correction and Registration of Image Data -- 2.1 Sources of Radiometric Distortion -- 2.1.1 The Effect of the Atmosphere on Radiation -- 2.1.2 Atmospheric Effects on Remote Sensing Imagery -- 2.1.3 Instrumentation Errors -- 2.2 Correction of Radiometric Distortion -- 2.2.1 Detailed Correction of Atmospheric Effects -- 2.2.2 Bulk Correction of Atmospheric Effects -- 2.2.3 Correction of Instrumentation Errors -- 2.3 Sources of Geometric Distortion -- 2.3.1 Earth Rotation Effects -- 2.3.2 Panoramic Distortion -- 2.3.3 Earth Curvature -- 2.3.4 Scan Time Skew -- 2.3.5 Variations in Platform Altitude, Velocity and Attitude -- 2.3.6 Aspect Ratio Distortion -- 2.3.7 Sensor Scan Nonlinearities -- 2.4 Correction of Geometric Distortion -- 2.4.1 Use of Mapping Polynomials for Image Correction -- 2.4.1.1 Mapping Polynomials and Ground Control Points -- 2.4.1.2 Resampling -- 2.4.1.3 Interpolation -- 2.4.1.4 Choice of Control Points -- 2.4.1.5 Example of Registration to a Map Grid -- 2.4.2 Mathematical Modelling -- 2.4.2.1 Aspect Ratio Correction -- 2.4.2.2 Earth Rotation Skew Correction -- 2.4.2.3 Image Orientation to North-South -- 2.4.2.4 Correction of Panoramic Effects -- 2.4.2.5 Combining the Corrections -- 2.5 Image Registration -- 2.5.1 Georeferencing and Geocoding -- 2.5.2 Image to Image Registration -- 2.5.3 Sequential Similarity Detection Algorithm -- 2.5.4 Example of Image to Image Registration -- 2.6 Miscellaneous Image Geometry Operations -- 2.6.1 Image Rotation -- 2.6.2 Scale Changing and Zooming -- References for Chapter 2 -- Problems -- 3 — The Interpretation of Digital Image Data -- 3.1 Two Approaches to Interpretation -- 3.2 Forms of Imagery for Photointerpretation -- 3.3 Computer Processing for Photointerpretation -- 3.4 An Introduction to Quantitative Analysis — Classification -- 3.5 Multispectral Space and Spectral Classes -- 3.6 Quantitative Analysis by Pattern Recognition -- 3.6.1 Pixel Vectors and Labelling -- 3.6.2 Unsupervised Classification -- 3.6.3 Supervised Classification -- References for Chapter 3 -- Problems -- 4 — Radiometric Enhancement Techniques -- 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 in Image Data -- 4.3.1 Histogram Modification Rule -- 4.3.2 Linear Contrast Enhancement -- 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 Equalization -- 4.4.1 Use of the Cumulative Histogram -- 4.4.2 Anomalies in Histogram Equalization -- 4.5 Histogram Matching -- 4.5.1 Principle of Histogram Matching -- 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 -- References for Chapter 4 -- Problems -- 5 — Geometric Enhancement Using Image Domain Techniques -- 5.1 Neighbourhood Operations -- 5.2 Template Operators -- 5.3 Geometric Enhancement as a Convolution Operation -- 5.4 ImageDomain Versus Fourier Transformation Approaches -- 5.5 Image Smoothing (Low Pass Filtering) -- 5.5.1 Mean Value Smoothing -- 5.5.2 Median Filtering -- 5.6 Edge Detection and Enhancement -- 5.6.1 Linear Edge Detecting Templates -- 5.6.2 Spatial Derivative Techniques -- 5.6.2.1 The Roberts Operator -- 5.6.2.2 The Sobel Operator -- 5.6.3 Thinning, Linking and Edge Responses -- 5.6.4 Edge Enhancement by Subtractive Smoothing -- 5.7 Line Detection -- 5.7.1 Linear Line Detecting Templates -- 5.7.2 Non-linear and Semi-linear Line Detecting Templates -- 5.8 General Convolution Filtering -- 5.9 Shape Detection -- References for Chapter 5 -- Problems -- 6 — Multispectral Transformations of Image Data -- 6.1 The Principal Components Transformation -- 6.1.1 The Mean Vector and Covariance Matrix -- 6.1.2 A Zero Correlation, Rotational Transform -- 6.1.3 An Example — Some Practical Considerations -- 6.1.4 The Effect of an Origin Shift -- 6.1.5 Application of Principal Components in Image Enhancement and Display -- 6.1.6 The Taylor Method of Contrast Enhancement -- 6.1.7 Other Applications of Principal Components Analysis -- 6.2 The Kauth-Thomas Tasseled Cap Transformation -- 6.3 Image Arithmetic, Band Ratios and Vegetation Indices -- References for Chapter 6 -- Problems -- 7 — Fourier Transformation of Image Data -- 7.1 Introduction -- 7.2 Special Functions -- 7.2.1 The Complex Exponential Function -- 7.2.2 The Dirac Delta Function -- 7.2.2.1 Properties of the Delta Function -- 7.2.3 The Heaviside Step Function -- 7.3 Fourier Series -- 7.4 The Fourier Transform -- 7.5 Convolution -- 7.5.1 The Convolution Integral -- 7.5.2 Convolution with an Impulse -- 7.5.3 The Convolution Theorem -- 7.6 Sampling Theory -- 7.7 The Discrete Fourier Transform -- 7.7.1 The Discrete Spectrum -- 7.7.2 Discrete Fourier Transform Formulae -- 7.7.3 Properties of the Discrete Fourier Transform -- 7.7.4 Computation of the Discrete Fourier Transform -- 7.7.5 Development of the Fast Fourier Transform Algorithm -- 7.7.6 Computational Cost of the Fast Fourier Transform -- 7.7.7 Bit Shuffling and Storage Considerations -- 7.8 The Discrete Fourier Transform of an Image -- 7.8.1 Definition -- 7.8.2 Evaluation of the Two Dimensional, Discrete Fourier Transform -- 7.8.3 The Concept of Spatial Frequency -- 7.8.4 Image Filtering for Geometric Enhancement -- 7.8.5 Convolution in Two Dimensions -- 7.9 Concluding Remarks -- References for Chapter 7 -- Problems -- Chapters 8—Supervised Classification Techniques -- I. Standard Classification Algorithms -- 8.1 Steps in Supervised Classification -- 8.2 Maximum Likelihood Classification -- 8.2.1 Bayes’Classification -- 8.2.2 The Maximum Likelihood Decision Rule -- 8.2.3 Multivariate Normal Class Models -- 8.2.4 Decision Surfaces -- 8.2.5 Thresholds -- 8.2.6 Number of Training Pixels Required for Each Class -- 8.2.7 A Simple Illustration -- 8.3 Minimum Distance Classification -- 8.3.1 The Case of Limited Training Data -- 8.3.2 The Discriminant Function -- 8.3.3 Degeneration of Maximum Likelihood to Minimum Distance Classification -- 8.3.4 Decision Surfaces -- 8.3.5 Thresholds -- 8.4 Parallelepiped Classification -- 8.5 Classification Time Comparison of the Classifiers -- 8.6 The Mahalanobis Classifier -- 8.7 Table Look Up Classification -- II. More Advanced Considerations -- 8.8 Context Classification -- 8.8.1 The Concept of Spatial Context -- 8.8.2 Context Classification by Image Pre-Processing -- 8.8.3 Post Classification Filtering -- 8.8.4 Probabilistic Label Relaxation -- 8.8.4.1 The Basic Algorithm -- 8.8.4.2 The Neighbourhood Function -- 8.8.4.3 Determining the Compatibility Coefficients -- 8.8.4.4 The Final Step – Stopping the Process -- 8.8.4.5 Examples -- 8.9 Classification of Mixed Image Data -- 8.9.1 The Stacked Vector Approach -- 8.9.2 Statistical Methods -- 8.9.3 The Theory of Evidence -- 8.9.3.1 The Concept of Evidential Mass -- 8.9.3.2 Combining Evidence – the Orthogonal Sum -- 8.9.3.3 Decision Rule -- 8.10 Classification Using Neural Networks -- 8.10.1 Linear Discrimination -- 8.10.1.1 Concept of a Weight Vector -- 8.10.1.2 Testing Class Membership -- 8.10.1.3 Training -- 8.10.1.4 Setting the Correction Increment -- 8.10.1.5 Classification – The Threshold Logic Unit -- 8.10.1.6 Multicategory Classification -- 8.10.2 Networks of Classifiers – Solutions of Nonlinear Problems -- 8.10.3 The Neural Network Approach -- 8.10.3.1 The Processing Element -- 8.10.3.2 Training the Neural Network – Backpropagation -- 8.10.3.3 Choosing the Network Parameters -- 8.10.3.4 Examples -- References for Chapter 8 -- Problems -- 9 — Clustering and Unsupervised Classification -- 9.1 Delineation of Spectral Classes -- 9.2 Similarity Metrics and Clustering Criteria -- 9.3 The Iterative Optimization (Migrating Means) Clustering Algorithm -- 9.3.1 The Basic . |
| Record Nr. | UNINA-9910480331403321 |
|
Richards John A
|
||
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Remote Sensing Digital Image Analysis : An Introduction / / John A. Richards
| Remote Sensing Digital Image Analysis : An Introduction / / John A. Richards |
| Autore | Richards John A |
| Edizione | [Second, revised and enlarged edition 1993.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1993 |
| Descrizione fisica | 1 online resource (xx, 340 pages) : 174 illustrations |
| Disciplina | 621.36/78 |
| Soggetto topico |
Geographical information systems
Refuse and refuse disposal Water pollution Air pollution Soil science Soil conservation Noise control Geographical Information Systems/Cartography Waste Management/Waste Technology Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Soil Science & Conservation Noise Control |
| ISBN | 3-642-88087-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 — Sources and Characteristics of Remote Sensing Image Data -- 1.1 Introduction to Data Sources -- 1.1.1 Characteristics of Digital Image Data -- 1.1.2 Spectral Ranges Commonly Used in Remote Sensing -- 1.1.3 Concluding Remarks -- 1.2 Weather Satellite Sensors -- 1.2.1 Polar Orbiting and Geosynchronous Satellites -- 1.2.2 The NOAA AVHRR (Advanced Very High Resolution Radiometer) -- 1.2.3 The Nimbus CZCS (Coastal Zone Colour Scanner) -- 1.2.4 GMS VISSR (Visible and Infrared Spin Scan Radiometer) -- 1.3 Earth Resource Satellite Sensors in the Visible and Infrared Regions -- 1.3.1 The Landsat System -- 1.3.2 The Landsat Instrument Complement -- 1.3.3 The Return Beam Vidicon(RBV) -- 1.3.4 The Multispectral Scanner (MSS) -- 1.3.5 The Thematic Mapper (TM) -- 1.3.6 The SPOT High Resolution Visible (HRV) Imaging Instrument -- 1.3.7 The Skylab S 192 Multispectral Scanner -- 1.3.8 The Heat Capacity Mapping Radiometer (HCMR) -- 1.3.9 Marine Observation Satellite (MOS) -- 1.3.10 Indian Remote Sensing Satellite (IRS) -- 1.4 Aircraft Scanners in the Visible and Infrared Regions -- 1.4.1 General Considerations -- 1.4.2 The Daedalus AADS 1240/1260 Multispectral Line Scanner -- 1.4.3 The Airborne Thematic Mapper (ATM) -- 1.4.4 The Thermal Infrared Multispectral Scanner (TIMS) -- 1.4.5 The MDA MEIS-II Linear Array Aircraft Scanner -- 1.4.6 Imaging Spectrometers -- 1.5 Image Data Sources in the Microwave Region -- 1.5.1 Side Looking Airborne Radar and Synthetic Aperture Radar -- 1.5.2 TheSeasatSAR -- 1.5.3 Shuttle Imaging Radar-A (SIR-A) -- 1.5.4 Shuttle Imaging Radar-B(SIR-B) -- 1.5.5 ERS-1 -- 1.5.6 JERS-1 -- 1.5.7 Radarsat -- 1.5.8 Aircraft Imaging Radar Systems -- 1.6 Spatial Data Sources in General -- 1.6.1 Types of Spatial Data -- 1.6.2 Data Formats -- 1.6.3 Geographic Information Systems (GIS) -- 1.6.4 The Challenge to Image Processing and Analysis -- 1.7 A Comparison of Scales in Digital Image Data -- References for Chapter 1 -- Problems -- 2 — Error Correction and Registration of Image Data -- 2.1 Sources of Radiometric Distortion -- 2.1.1 The Effect of the Atmosphere on Radiation -- 2.1.2 Atmospheric Effects on Remote Sensing Imagery -- 2.1.3 Instrumentation Errors -- 2.2 Correction of Radiometric Distortion -- 2.2.1 Detailed Correction of Atmospheric Effects -- 2.2.2 Bulk Correction of Atmospheric Effects -- 2.2.3 Correction of Instrumentation Errors -- 2.3 Sources of Geometric Distortion -- 2.3.1 Earth Rotation Effects -- 2.3.2 Panoramic Distortion -- 2.3.3 Earth Curvature -- 2.3.4 Scan Time Skew -- 2.3.5 Variations in Platform Altitude, Velocity and Attitude -- 2.3.6 Aspect Ratio Distortion -- 2.3.7 Sensor Scan Nonlinearities -- 2.4 Correction of Geometric Distortion -- 2.4.1 Use of Mapping Polynomials for Image Correction -- 2.4.1.1 Mapping Polynomials and Ground Control Points -- 2.4.1.2 Resampling -- 2.4.1.3 Interpolation -- 2.4.1.4 Choice of Control Points -- 2.4.1.5 Example of Registration to a Map Grid -- 2.4.2 Mathematical Modelling -- 2.4.2.1 Aspect Ratio Correction -- 2.4.2.2 Earth Rotation Skew Correction -- 2.4.2.3 Image Orientation to North-South -- 2.4.2.4 Correction of Panoramic Effects -- 2.4.2.5 Combining the Corrections -- 2.5 Image Registration -- 2.5.1 Georeferencing and Geocoding -- 2.5.2 Image to Image Registration -- 2.5.3 Sequential Similarity Detection Algorithm -- 2.5.4 Example of Image to Image Registration -- 2.6 Miscellaneous Image Geometry Operations -- 2.6.1 Image Rotation -- 2.6.2 Scale Changing and Zooming -- References for Chapter 2 -- Problems -- 3 — The Interpretation of Digital Image Data -- 3.1 Two Approaches to Interpretation -- 3.2 Forms of Imagery for Photointerpretation -- 3.3 Computer Processing for Photointerpretation -- 3.4 An Introduction to Quantitative Analysis — Classification -- 3.5 Multispectral Space and Spectral Classes -- 3.6 Quantitative Analysis by Pattern Recognition -- 3.6.1 Pixel Vectors and Labelling -- 3.6.2 Unsupervised Classification -- 3.6.3 Supervised Classification -- References for Chapter 3 -- Problems -- 4 — Radiometric Enhancement Techniques -- 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 in Image Data -- 4.3.1 Histogram Modification Rule -- 4.3.2 Linear Contrast Enhancement -- 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 Equalization -- 4.4.1 Use of the Cumulative Histogram -- 4.4.2 Anomalies in Histogram Equalization -- 4.5 Histogram Matching -- 4.5.1 Principle of Histogram Matching -- 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 -- References for Chapter 4 -- Problems -- 5 — Geometric Enhancement Using Image Domain Techniques -- 5.1 Neighbourhood Operations -- 5.2 Template Operators -- 5.3 Geometric Enhancement as a Convolution Operation -- 5.4 ImageDomain Versus Fourier Transformation Approaches -- 5.5 Image Smoothing (Low Pass Filtering) -- 5.5.1 Mean Value Smoothing -- 5.5.2 Median Filtering -- 5.6 Edge Detection and Enhancement -- 5.6.1 Linear Edge Detecting Templates -- 5.6.2 Spatial Derivative Techniques -- 5.6.2.1 The Roberts Operator -- 5.6.2.2 The Sobel Operator -- 5.6.3 Thinning, Linking and Edge Responses -- 5.6.4 Edge Enhancement by Subtractive Smoothing -- 5.7 Line Detection -- 5.7.1 Linear Line Detecting Templates -- 5.7.2 Non-linear and Semi-linear Line Detecting Templates -- 5.8 General Convolution Filtering -- 5.9 Shape Detection -- References for Chapter 5 -- Problems -- 6 — Multispectral Transformations of Image Data -- 6.1 The Principal Components Transformation -- 6.1.1 The Mean Vector and Covariance Matrix -- 6.1.2 A Zero Correlation, Rotational Transform -- 6.1.3 An Example — Some Practical Considerations -- 6.1.4 The Effect of an Origin Shift -- 6.1.5 Application of Principal Components in Image Enhancement and Display -- 6.1.6 The Taylor Method of Contrast Enhancement -- 6.1.7 Other Applications of Principal Components Analysis -- 6.2 The Kauth-Thomas Tasseled Cap Transformation -- 6.3 Image Arithmetic, Band Ratios and Vegetation Indices -- References for Chapter 6 -- Problems -- 7 — Fourier Transformation of Image Data -- 7.1 Introduction -- 7.2 Special Functions -- 7.2.1 The Complex Exponential Function -- 7.2.2 The Dirac Delta Function -- 7.2.2.1 Properties of the Delta Function -- 7.2.3 The Heaviside Step Function -- 7.3 Fourier Series -- 7.4 The Fourier Transform -- 7.5 Convolution -- 7.5.1 The Convolution Integral -- 7.5.2 Convolution with an Impulse -- 7.5.3 The Convolution Theorem -- 7.6 Sampling Theory -- 7.7 The Discrete Fourier Transform -- 7.7.1 The Discrete Spectrum -- 7.7.2 Discrete Fourier Transform Formulae -- 7.7.3 Properties of the Discrete Fourier Transform -- 7.7.4 Computation of the Discrete Fourier Transform -- 7.7.5 Development of the Fast Fourier Transform Algorithm -- 7.7.6 Computational Cost of the Fast Fourier Transform -- 7.7.7 Bit Shuffling and Storage Considerations -- 7.8 The Discrete Fourier Transform of an Image -- 7.8.1 Definition -- 7.8.2 Evaluation of the Two Dimensional, Discrete Fourier Transform -- 7.8.3 The Concept of Spatial Frequency -- 7.8.4 Image Filtering for Geometric Enhancement -- 7.8.5 Convolution in Two Dimensions -- 7.9 Concluding Remarks -- References for Chapter 7 -- Problems -- Chapters 8—Supervised Classification Techniques -- I. Standard Classification Algorithms -- 8.1 Steps in Supervised Classification -- 8.2 Maximum Likelihood Classification -- 8.2.1 Bayes’Classification -- 8.2.2 The Maximum Likelihood Decision Rule -- 8.2.3 Multivariate Normal Class Models -- 8.2.4 Decision Surfaces -- 8.2.5 Thresholds -- 8.2.6 Number of Training Pixels Required for Each Class -- 8.2.7 A Simple Illustration -- 8.3 Minimum Distance Classification -- 8.3.1 The Case of Limited Training Data -- 8.3.2 The Discriminant Function -- 8.3.3 Degeneration of Maximum Likelihood to Minimum Distance Classification -- 8.3.4 Decision Surfaces -- 8.3.5 Thresholds -- 8.4 Parallelepiped Classification -- 8.5 Classification Time Comparison of the Classifiers -- 8.6 The Mahalanobis Classifier -- 8.7 Table Look Up Classification -- II. More Advanced Considerations -- 8.8 Context Classification -- 8.8.1 The Concept of Spatial Context -- 8.8.2 Context Classification by Image Pre-Processing -- 8.8.3 Post Classification Filtering -- 8.8.4 Probabilistic Label Relaxation -- 8.8.4.1 The Basic Algorithm -- 8.8.4.2 The Neighbourhood Function -- 8.8.4.3 Determining the Compatibility Coefficients -- 8.8.4.4 The Final Step – Stopping the Process -- 8.8.4.5 Examples -- 8.9 Classification of Mixed Image Data -- 8.9.1 The Stacked Vector Approach -- 8.9.2 Statistical Methods -- 8.9.3 The Theory of Evidence -- 8.9.3.1 The Concept of Evidential Mass -- 8.9.3.2 Combining Evidence – the Orthogonal Sum -- 8.9.3.3 Decision Rule -- 8.10 Classification Using Neural Networks -- 8.10.1 Linear Discrimination -- 8.10.1.1 Concept of a Weight Vector -- 8.10.1.2 Testing Class Membership -- 8.10.1.3 Training -- 8.10.1.4 Setting the Correction Increment -- 8.10.1.5 Classification – The Threshold Logic Unit -- 8.10.1.6 Multicategory Classification -- 8.10.2 Networks of Classifiers – Solutions of Nonlinear Problems -- 8.10.3 The Neural Network Approach -- 8.10.3.1 The Processing Element -- 8.10.3.2 Training the Neural Network – Backpropagation -- 8.10.3.3 Choosing the Network Parameters -- 8.10.3.4 Examples -- References for Chapter 8 -- Problems -- 9 — Clustering and Unsupervised Classification -- 9.1 Delineation of Spectral Classes -- 9.2 Similarity Metrics and Clustering Criteria -- 9.3 The Iterative Optimization (Migrating Means) Clustering Algorithm -- 9.3.1 The Basic . |
| Record Nr. | UNINA-9910789208603321 |
|
Richards John A
|
||
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Remote Sensing Digital Image Analysis : An Introduction / / John A. Richards
| Remote Sensing Digital Image Analysis : An Introduction / / John A. Richards |
| Autore | Richards John A |
| Edizione | [Second, revised and enlarged edition 1993.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1993 |
| Descrizione fisica | 1 online resource (xx, 340 pages) : 174 illustrations |
| Disciplina | 621.36/78 |
| Soggetto topico |
Geographical information systems
Refuse and refuse disposal Water pollution Air pollution Soil science Soil conservation Noise control Geographical Information Systems/Cartography Waste Management/Waste Technology Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Soil Science & Conservation Noise Control |
| ISBN | 3-642-88087-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 — Sources and Characteristics of Remote Sensing Image Data -- 1.1 Introduction to Data Sources -- 1.1.1 Characteristics of Digital Image Data -- 1.1.2 Spectral Ranges Commonly Used in Remote Sensing -- 1.1.3 Concluding Remarks -- 1.2 Weather Satellite Sensors -- 1.2.1 Polar Orbiting and Geosynchronous Satellites -- 1.2.2 The NOAA AVHRR (Advanced Very High Resolution Radiometer) -- 1.2.3 The Nimbus CZCS (Coastal Zone Colour Scanner) -- 1.2.4 GMS VISSR (Visible and Infrared Spin Scan Radiometer) -- 1.3 Earth Resource Satellite Sensors in the Visible and Infrared Regions -- 1.3.1 The Landsat System -- 1.3.2 The Landsat Instrument Complement -- 1.3.3 The Return Beam Vidicon(RBV) -- 1.3.4 The Multispectral Scanner (MSS) -- 1.3.5 The Thematic Mapper (TM) -- 1.3.6 The SPOT High Resolution Visible (HRV) Imaging Instrument -- 1.3.7 The Skylab S 192 Multispectral Scanner -- 1.3.8 The Heat Capacity Mapping Radiometer (HCMR) -- 1.3.9 Marine Observation Satellite (MOS) -- 1.3.10 Indian Remote Sensing Satellite (IRS) -- 1.4 Aircraft Scanners in the Visible and Infrared Regions -- 1.4.1 General Considerations -- 1.4.2 The Daedalus AADS 1240/1260 Multispectral Line Scanner -- 1.4.3 The Airborne Thematic Mapper (ATM) -- 1.4.4 The Thermal Infrared Multispectral Scanner (TIMS) -- 1.4.5 The MDA MEIS-II Linear Array Aircraft Scanner -- 1.4.6 Imaging Spectrometers -- 1.5 Image Data Sources in the Microwave Region -- 1.5.1 Side Looking Airborne Radar and Synthetic Aperture Radar -- 1.5.2 TheSeasatSAR -- 1.5.3 Shuttle Imaging Radar-A (SIR-A) -- 1.5.4 Shuttle Imaging Radar-B(SIR-B) -- 1.5.5 ERS-1 -- 1.5.6 JERS-1 -- 1.5.7 Radarsat -- 1.5.8 Aircraft Imaging Radar Systems -- 1.6 Spatial Data Sources in General -- 1.6.1 Types of Spatial Data -- 1.6.2 Data Formats -- 1.6.3 Geographic Information Systems (GIS) -- 1.6.4 The Challenge to Image Processing and Analysis -- 1.7 A Comparison of Scales in Digital Image Data -- References for Chapter 1 -- Problems -- 2 — Error Correction and Registration of Image Data -- 2.1 Sources of Radiometric Distortion -- 2.1.1 The Effect of the Atmosphere on Radiation -- 2.1.2 Atmospheric Effects on Remote Sensing Imagery -- 2.1.3 Instrumentation Errors -- 2.2 Correction of Radiometric Distortion -- 2.2.1 Detailed Correction of Atmospheric Effects -- 2.2.2 Bulk Correction of Atmospheric Effects -- 2.2.3 Correction of Instrumentation Errors -- 2.3 Sources of Geometric Distortion -- 2.3.1 Earth Rotation Effects -- 2.3.2 Panoramic Distortion -- 2.3.3 Earth Curvature -- 2.3.4 Scan Time Skew -- 2.3.5 Variations in Platform Altitude, Velocity and Attitude -- 2.3.6 Aspect Ratio Distortion -- 2.3.7 Sensor Scan Nonlinearities -- 2.4 Correction of Geometric Distortion -- 2.4.1 Use of Mapping Polynomials for Image Correction -- 2.4.1.1 Mapping Polynomials and Ground Control Points -- 2.4.1.2 Resampling -- 2.4.1.3 Interpolation -- 2.4.1.4 Choice of Control Points -- 2.4.1.5 Example of Registration to a Map Grid -- 2.4.2 Mathematical Modelling -- 2.4.2.1 Aspect Ratio Correction -- 2.4.2.2 Earth Rotation Skew Correction -- 2.4.2.3 Image Orientation to North-South -- 2.4.2.4 Correction of Panoramic Effects -- 2.4.2.5 Combining the Corrections -- 2.5 Image Registration -- 2.5.1 Georeferencing and Geocoding -- 2.5.2 Image to Image Registration -- 2.5.3 Sequential Similarity Detection Algorithm -- 2.5.4 Example of Image to Image Registration -- 2.6 Miscellaneous Image Geometry Operations -- 2.6.1 Image Rotation -- 2.6.2 Scale Changing and Zooming -- References for Chapter 2 -- Problems -- 3 — The Interpretation of Digital Image Data -- 3.1 Two Approaches to Interpretation -- 3.2 Forms of Imagery for Photointerpretation -- 3.3 Computer Processing for Photointerpretation -- 3.4 An Introduction to Quantitative Analysis — Classification -- 3.5 Multispectral Space and Spectral Classes -- 3.6 Quantitative Analysis by Pattern Recognition -- 3.6.1 Pixel Vectors and Labelling -- 3.6.2 Unsupervised Classification -- 3.6.3 Supervised Classification -- References for Chapter 3 -- Problems -- 4 — Radiometric Enhancement Techniques -- 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 in Image Data -- 4.3.1 Histogram Modification Rule -- 4.3.2 Linear Contrast Enhancement -- 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 Equalization -- 4.4.1 Use of the Cumulative Histogram -- 4.4.2 Anomalies in Histogram Equalization -- 4.5 Histogram Matching -- 4.5.1 Principle of Histogram Matching -- 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 -- References for Chapter 4 -- Problems -- 5 — Geometric Enhancement Using Image Domain Techniques -- 5.1 Neighbourhood Operations -- 5.2 Template Operators -- 5.3 Geometric Enhancement as a Convolution Operation -- 5.4 ImageDomain Versus Fourier Transformation Approaches -- 5.5 Image Smoothing (Low Pass Filtering) -- 5.5.1 Mean Value Smoothing -- 5.5.2 Median Filtering -- 5.6 Edge Detection and Enhancement -- 5.6.1 Linear Edge Detecting Templates -- 5.6.2 Spatial Derivative Techniques -- 5.6.2.1 The Roberts Operator -- 5.6.2.2 The Sobel Operator -- 5.6.3 Thinning, Linking and Edge Responses -- 5.6.4 Edge Enhancement by Subtractive Smoothing -- 5.7 Line Detection -- 5.7.1 Linear Line Detecting Templates -- 5.7.2 Non-linear and Semi-linear Line Detecting Templates -- 5.8 General Convolution Filtering -- 5.9 Shape Detection -- References for Chapter 5 -- Problems -- 6 — Multispectral Transformations of Image Data -- 6.1 The Principal Components Transformation -- 6.1.1 The Mean Vector and Covariance Matrix -- 6.1.2 A Zero Correlation, Rotational Transform -- 6.1.3 An Example — Some Practical Considerations -- 6.1.4 The Effect of an Origin Shift -- 6.1.5 Application of Principal Components in Image Enhancement and Display -- 6.1.6 The Taylor Method of Contrast Enhancement -- 6.1.7 Other Applications of Principal Components Analysis -- 6.2 The Kauth-Thomas Tasseled Cap Transformation -- 6.3 Image Arithmetic, Band Ratios and Vegetation Indices -- References for Chapter 6 -- Problems -- 7 — Fourier Transformation of Image Data -- 7.1 Introduction -- 7.2 Special Functions -- 7.2.1 The Complex Exponential Function -- 7.2.2 The Dirac Delta Function -- 7.2.2.1 Properties of the Delta Function -- 7.2.3 The Heaviside Step Function -- 7.3 Fourier Series -- 7.4 The Fourier Transform -- 7.5 Convolution -- 7.5.1 The Convolution Integral -- 7.5.2 Convolution with an Impulse -- 7.5.3 The Convolution Theorem -- 7.6 Sampling Theory -- 7.7 The Discrete Fourier Transform -- 7.7.1 The Discrete Spectrum -- 7.7.2 Discrete Fourier Transform Formulae -- 7.7.3 Properties of the Discrete Fourier Transform -- 7.7.4 Computation of the Discrete Fourier Transform -- 7.7.5 Development of the Fast Fourier Transform Algorithm -- 7.7.6 Computational Cost of the Fast Fourier Transform -- 7.7.7 Bit Shuffling and Storage Considerations -- 7.8 The Discrete Fourier Transform of an Image -- 7.8.1 Definition -- 7.8.2 Evaluation of the Two Dimensional, Discrete Fourier Transform -- 7.8.3 The Concept of Spatial Frequency -- 7.8.4 Image Filtering for Geometric Enhancement -- 7.8.5 Convolution in Two Dimensions -- 7.9 Concluding Remarks -- References for Chapter 7 -- Problems -- Chapters 8—Supervised Classification Techniques -- I. Standard Classification Algorithms -- 8.1 Steps in Supervised Classification -- 8.2 Maximum Likelihood Classification -- 8.2.1 Bayes’Classification -- 8.2.2 The Maximum Likelihood Decision Rule -- 8.2.3 Multivariate Normal Class Models -- 8.2.4 Decision Surfaces -- 8.2.5 Thresholds -- 8.2.6 Number of Training Pixels Required for Each Class -- 8.2.7 A Simple Illustration -- 8.3 Minimum Distance Classification -- 8.3.1 The Case of Limited Training Data -- 8.3.2 The Discriminant Function -- 8.3.3 Degeneration of Maximum Likelihood to Minimum Distance Classification -- 8.3.4 Decision Surfaces -- 8.3.5 Thresholds -- 8.4 Parallelepiped Classification -- 8.5 Classification Time Comparison of the Classifiers -- 8.6 The Mahalanobis Classifier -- 8.7 Table Look Up Classification -- II. More Advanced Considerations -- 8.8 Context Classification -- 8.8.1 The Concept of Spatial Context -- 8.8.2 Context Classification by Image Pre-Processing -- 8.8.3 Post Classification Filtering -- 8.8.4 Probabilistic Label Relaxation -- 8.8.4.1 The Basic Algorithm -- 8.8.4.2 The Neighbourhood Function -- 8.8.4.3 Determining the Compatibility Coefficients -- 8.8.4.4 The Final Step – Stopping the Process -- 8.8.4.5 Examples -- 8.9 Classification of Mixed Image Data -- 8.9.1 The Stacked Vector Approach -- 8.9.2 Statistical Methods -- 8.9.3 The Theory of Evidence -- 8.9.3.1 The Concept of Evidential Mass -- 8.9.3.2 Combining Evidence – the Orthogonal Sum -- 8.9.3.3 Decision Rule -- 8.10 Classification Using Neural Networks -- 8.10.1 Linear Discrimination -- 8.10.1.1 Concept of a Weight Vector -- 8.10.1.2 Testing Class Membership -- 8.10.1.3 Training -- 8.10.1.4 Setting the Correction Increment -- 8.10.1.5 Classification – The Threshold Logic Unit -- 8.10.1.6 Multicategory Classification -- 8.10.2 Networks of Classifiers – Solutions of Nonlinear Problems -- 8.10.3 The Neural Network Approach -- 8.10.3.1 The Processing Element -- 8.10.3.2 Training the Neural Network – Backpropagation -- 8.10.3.3 Choosing the Network Parameters -- 8.10.3.4 Examples -- References for Chapter 8 -- Problems -- 9 — Clustering and Unsupervised Classification -- 9.1 Delineation of Spectral Classes -- 9.2 Similarity Metrics and Clustering Criteria -- 9.3 The Iterative Optimization (Migrating Means) Clustering Algorithm -- 9.3.1 The Basic . |
| Record Nr. | UNINA-9910813300103321 |
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Richards John A
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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