Image Analysis, Classification, and Change Detection in Remote Sensing
With Algorithms for ENVI/IDL, Second Edition
2nd Edition
Published on 1. December 2009
Book
Hardback
472 pages
978-1-4200-8713-0 (ISBN)
Description
Demonstrating the breadth and depth of growth in the field since the publication of the popular first edition, Image Analysis, Classification and Change Detection in Remote Sensing, with Algorithms for ENVI/IDL, Second Edition has been updated and expanded to keep pace with the latest versions of the ENVI software environment. Effectively interweaving theory, algorithms, and computer codes, the text supplies an accessible introduction to the techniques used in the processing of remotely sensed imagery.
This significantly expanded edition presents numerous image analysis examples and algorithms, all illustrated in the array-oriented language IDL-allowing readers to plug the illustrations and applications covered in the text directly into the ENVI system-in a completely transparent fashion. Revised chapters on image arrays, linear algebra, and statistics convey the required foundation, while updated chapters detail kernel methods for principal component analysis, kernel-based clustering, and classification with support vector machines.
Additions to this edition include:
An introduction to mutual information and entropy
Algorithms and code for image segmentation
In-depth treatment of ensemble classification (adaptive boosting )
Improved IDL code for all ENVI extensions, with routines that can take advantage of the parallel computational power of modern graphics processors
Code that runs on all versions of the ENVI/IDL software environment from ENVI 4.1 up to the present-available on the author's website
Many new end-of-chapter exercises and programming projects
With its numerous programming examples in IDL and many applications supporting ENVI, such as data fusion, statistical change detection, clustering and supervised classification with neural networks-all available as downloadable source code-this self-contained text is ideal for classroom use or self study.
This significantly expanded edition presents numerous image analysis examples and algorithms, all illustrated in the array-oriented language IDL-allowing readers to plug the illustrations and applications covered in the text directly into the ENVI system-in a completely transparent fashion. Revised chapters on image arrays, linear algebra, and statistics convey the required foundation, while updated chapters detail kernel methods for principal component analysis, kernel-based clustering, and classification with support vector machines.
Additions to this edition include:
An introduction to mutual information and entropy
Algorithms and code for image segmentation
In-depth treatment of ensemble classification (adaptive boosting )
Improved IDL code for all ENVI extensions, with routines that can take advantage of the parallel computational power of modern graphics processors
Code that runs on all versions of the ENVI/IDL software environment from ENVI 4.1 up to the present-available on the author's website
Many new end-of-chapter exercises and programming projects
With its numerous programming examples in IDL and many applications supporting ENVI, such as data fusion, statistical change detection, clustering and supervised classification with neural networks-all available as downloadable source code-this self-contained text is ideal for classroom use or self study.
More details
Edition
2nd New edition
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
University students working with remote sensing data in digital image analysis and GIS environments., GIS professionals, and researchers active in the field.
Edition type
New edition
Product notice
Paper over boards
Illustrations
16 page color insert follows page 114, 136 s/w Abbildungen, 16 farbige Abbildungen, 6 s/w Tabellen
16 page color insert follows page 114; 594 Equations, 54 in text boxes; 6 Tables, black and white; 16 Illustrations, color; 136 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 159 mm
Weight
794 gr
ISBN-13
978-1-4200-8713-0 (9781420087130)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Morton John Canty
Image Analysis, Classification and Change Detection in Remote Sensing
With Algorithms for ENVI/IDL and Python, Third Edition
Book
06/2014
3rd Edition
CRC Press
€171.12
Article exhausted; check for reprint
Previous edition
Morton J. Canty
Image Analysis, Classification and Change Detection in Remote Sensing
With Algorithms for ENVI/IDL
Book
08/2006
1st Edition
CRC Press
€84.31
Article exhausted; check for reprint
Person
Content
Images, Arrays, and Matrices
Multispectral Satellite Images
Algebra of Vectors and Matrices
Eigenvalues and Eigenvectors
Singular Value Decomposition
Vector Derivatives
Finding Minima and Maxima
Image Statistics
Random Variables
Random Vectors
Parameter Estimation
Hypothesis Testing and Sample Distribution Functions
Conditional Probabilities, Bayes' Theorem, and Classification
Ordinary Linear Regression
Entropy and Information
Transformations
Discrete Fourier Transform
Discrete Wavelet Transform
Principal Components
Minimum Noise Fraction
Spatial Correlation
Filters, Kernels, and Fields
Convolution Theorem
Linear Filters
Wavelets and Filter Banks
Kernel Methods
Gibbs-Markov Random Fields
Image Enhancement and Correction
Lookup Tables and Histogram Functions
Filtering and Feature Extraction
Panchromatic Sharpening
Topographic Correction
Image-Image Registration
Supervised Classification: Part 1
Maximum a Posteriori Probability
Training Data and Separability
Maximum Likelihood Classification
Gaussian Kernel Classification
Neural Networks
Support Vector Machines
Supervised Classification: Part 2
Postprocessing
Evaluation and Comparison of Classification Accuracy
Adaptive Boosting
Hyperspectral Analysis
Unsupervised Classification
Simple Cost Functions
Algorithms That Minimize the Simple Cost Functions
Gaussian Mixture Clustering
Including Spatial Information
Benchmark
Kohonen Self-Organizing Map
Image Segmentation
Change Detection
Algebraic Methods
Postclassification Comparison
Principal Components Analysis
Multivariate Alteration Detection
Decision Thresholds and Unsupervised Classification of Changes
Radiometric Normalization
Appendix A: Mathematical Tools
Cholesky Decomposition
Vector and Inner Product Spaces
Least Squares Procedures
Appendix B: Efficient Neural Network Training Algorithms
Hessian Matrix
Scaled Conjugate Gradient Training
Kalman Filter Training
A Neural Network Classifier with Hybrid Training
Appendix C: ENVI Extensions in IDL
Installation
Extensions
Appendix D: Mathematical Notation
References
Index
Multispectral Satellite Images
Algebra of Vectors and Matrices
Eigenvalues and Eigenvectors
Singular Value Decomposition
Vector Derivatives
Finding Minima and Maxima
Image Statistics
Random Variables
Random Vectors
Parameter Estimation
Hypothesis Testing and Sample Distribution Functions
Conditional Probabilities, Bayes' Theorem, and Classification
Ordinary Linear Regression
Entropy and Information
Transformations
Discrete Fourier Transform
Discrete Wavelet Transform
Principal Components
Minimum Noise Fraction
Spatial Correlation
Filters, Kernels, and Fields
Convolution Theorem
Linear Filters
Wavelets and Filter Banks
Kernel Methods
Gibbs-Markov Random Fields
Image Enhancement and Correction
Lookup Tables and Histogram Functions
Filtering and Feature Extraction
Panchromatic Sharpening
Topographic Correction
Image-Image Registration
Supervised Classification: Part 1
Maximum a Posteriori Probability
Training Data and Separability
Maximum Likelihood Classification
Gaussian Kernel Classification
Neural Networks
Support Vector Machines
Supervised Classification: Part 2
Postprocessing
Evaluation and Comparison of Classification Accuracy
Adaptive Boosting
Hyperspectral Analysis
Unsupervised Classification
Simple Cost Functions
Algorithms That Minimize the Simple Cost Functions
Gaussian Mixture Clustering
Including Spatial Information
Benchmark
Kohonen Self-Organizing Map
Image Segmentation
Change Detection
Algebraic Methods
Postclassification Comparison
Principal Components Analysis
Multivariate Alteration Detection
Decision Thresholds and Unsupervised Classification of Changes
Radiometric Normalization
Appendix A: Mathematical Tools
Cholesky Decomposition
Vector and Inner Product Spaces
Least Squares Procedures
Appendix B: Efficient Neural Network Training Algorithms
Hessian Matrix
Scaled Conjugate Gradient Training
Kalman Filter Training
A Neural Network Classifier with Hybrid Training
Appendix C: ENVI Extensions in IDL
Installation
Extensions
Appendix D: Mathematical Notation
References
Index