Image Analysis, Classification and Change Detection in Remote Sensing
With Algorithms for ENVI/IDL
1st Edition
Published on 1. August 2006
Book
Hardback
368 pages
978-0-8493-7251-3 (ISBN)
Description
With an ever-increasing availability of aerial and satellite Earth observation data, image analysis has become an essential part of remote sensing. Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL combines theory, algorithms, and computer codes and conveys required proficiency in vector algebra and basic statistics. It covers such topics as basic Fourier transforms, wavelets, principle components, minimum noise fraction transformation, and othorectification. The text also discusses panchromatic sharpening, explores multivariate change detection, examines supervised and unsupervised land cover classification and hyperspectral analysis. With programming examples in IDL and applications that support ENVI, it offers many extensions, such as for data fusion, statistical change detection, clustering and supervised classification with neural networks, all available as downloadable source code.
Focusing on pixel-oriented analysis of visual/infrared Earth observation satellite imagery, this book extends the ENVI interface in IDL in order to implement new methods and algorithms of arbitrary sophistication.
All of the illustrations and applications in the text are programmed in RSI's ENVI/IDL. The software and source code is available for download at: http://www.crcpress.com/product/isbn/9780849372513
Ideal for undergraduate and graduate student, this book provides exercises and small programming projects at the end of each chapter. A solutions manual is also available.
Focusing on pixel-oriented analysis of visual/infrared Earth observation satellite imagery, this book extends the ENVI interface in IDL in order to implement new methods and algorithms of arbitrary sophistication.
All of the illustrations and applications in the text are programmed in RSI's ENVI/IDL. The software and source code is available for download at: http://www.crcpress.com/product/isbn/9780849372513
Ideal for undergraduate and graduate student, this book provides exercises and small programming projects at the end of each chapter. A solutions manual is also available.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Advanced undergraduate, graduate level remote sensing digital image analysis.
Illustrations
17 farbige Abbildungen, 4 s/w Tabellen, 105 s/w Abbildungen
4 Tables, black and white; 17 Illustrations, color; 105 Illustrations, black and white
Dimensions
Height: 235 mm
Width: 156 mm
Weight
680 gr
ISBN-13
978-0-8493-7251-3 (9780849372513)
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 J. Canty
Image Analysis, Classification, and Change Detection in Remote Sensing
With Algorithms for ENVI/IDL, Second Edition
Book
12/2009
2nd Edition
CRC Press
€148.80
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Content
List of Figures
Program Listings
Images, Arrays, and Matrices
Multispectral satellite images
Algebra of vectors and matrices
Eigenvalues and eigenvectors
Vector derivatives
Finding minima and maxima
Exercises
Image Statistics
Random variables
Random vectors
Parameter estimation
Hypothesis testing and sample distribution functions
Bayes' Theorem and classification
Ordinary linear regression
Exercises
Transformations
The discrete Fourier transform
The discrete wavelet transform
Principal components
Maximum noise fraction
Spatial correlation
Exercises
Convolutions, Filters, and Fields
The Convolution Theorem
Linear filters
Wavelets and filter banks
Gibbs-Markov random fields
Exercises
Image Enhancement and Correction
Lookup tables and histogram functions
High-pass spatial filtering
Panchromatic sharpening
Topographic correction
Image-image registration
Exercises
Supervised Classification
Maximum a posteriori probability
Training data and separability
Maximum likelihood classification
Gaussian kernel classification
Neural networks
Postprocessing
Evaluation and comparison of classification accuracy
Hyperspectral analysis
Exercises
Unsupervised Classification
Simple cost functions
Algorithms that minimize the simple cost functions
Fuzzy maximum likelihood estimation clustering
Including spatial information
A benchmark
The Kohonen self-organizing map
Exercises
Change Detection
Algebraic methods
Principal components
Postclassification comparison
Multivariate alteration detection
Decision thresholds and unsupervised classification of changes
Radiometric normalization
Exercises
APPENDIX A: Mathematical Tools
Cholesky Decomposition
Least Squares Procedures
APPENDIX B: Efficient Neural Network Training Algorithms
The Hessian Matrix
Scaled Conjugate Gradient Training
Kalman Filter Training
A Neural Network Classifier with Hybrid Training
APPENDIX C: ENVI Extensions in IDL
Installation
Panchromatic Sharpening
Topographic Modeling and Registration
Supervised Classification
Unsupervised Classification
Change Detection
Mathematical Notation
References
Index
Program Listings
Images, Arrays, and Matrices
Multispectral satellite images
Algebra of vectors and matrices
Eigenvalues and eigenvectors
Vector derivatives
Finding minima and maxima
Exercises
Image Statistics
Random variables
Random vectors
Parameter estimation
Hypothesis testing and sample distribution functions
Bayes' Theorem and classification
Ordinary linear regression
Exercises
Transformations
The discrete Fourier transform
The discrete wavelet transform
Principal components
Maximum noise fraction
Spatial correlation
Exercises
Convolutions, Filters, and Fields
The Convolution Theorem
Linear filters
Wavelets and filter banks
Gibbs-Markov random fields
Exercises
Image Enhancement and Correction
Lookup tables and histogram functions
High-pass spatial filtering
Panchromatic sharpening
Topographic correction
Image-image registration
Exercises
Supervised Classification
Maximum a posteriori probability
Training data and separability
Maximum likelihood classification
Gaussian kernel classification
Neural networks
Postprocessing
Evaluation and comparison of classification accuracy
Hyperspectral analysis
Exercises
Unsupervised Classification
Simple cost functions
Algorithms that minimize the simple cost functions
Fuzzy maximum likelihood estimation clustering
Including spatial information
A benchmark
The Kohonen self-organizing map
Exercises
Change Detection
Algebraic methods
Principal components
Postclassification comparison
Multivariate alteration detection
Decision thresholds and unsupervised classification of changes
Radiometric normalization
Exercises
APPENDIX A: Mathematical Tools
Cholesky Decomposition
Least Squares Procedures
APPENDIX B: Efficient Neural Network Training Algorithms
The Hessian Matrix
Scaled Conjugate Gradient Training
Kalman Filter Training
A Neural Network Classifier with Hybrid Training
APPENDIX C: ENVI Extensions in IDL
Installation
Panchromatic Sharpening
Topographic Modeling and Registration
Supervised Classification
Unsupervised Classification
Change Detection
Mathematical Notation
References
Index