Image Analysis, Classification and Change Detection in Remote Sensing
With Algorithms for ENVI/IDL and Python, Third Edition
3rd Edition
Published on 6. June 2014
Book
Hardback
576 pages
978-1-4665-7037-5 (ISBN)
Description
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes.
See What's New in the Third Edition:
Inclusion of extensive code in Python, with a cloud computing example
New material on synthetic aperture radar (SAR) data analysis
New illustrations in all chapters
Extended theoretical development
The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power.
The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.
See What's New in the Third Edition:
Inclusion of extensive code in Python, with a cloud computing example
New material on synthetic aperture radar (SAR) data analysis
New illustrations in all chapters
Extended theoretical development
The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power.
The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.
Reviews / Votes
"Dr. Canty continues to update his excellent remote sensing book to use modern computing techniques; this time adding scripts in the open source Python complementing his previous IDL/ENVI examples. This is a great reference for those looking to put remote sensing theory into practice."-Michael Galloy, Tech-X Corporation
"... includes 1) open source (Python) code, making the book more useful to readers without commercial software licenses, and 2) material on polarimetric SAR imagery, an increasingly important field of remote sensing, while continuing to focus on statistically motivated, data driven analysis methods. With this third edition Mort Canty's book has become even more indispensable."
-Allan Aasbjerg Nielsen, Technical University of Denmark
"... the addition of open source Python code along with IDL will certainly guarantee a larger readership. For students/practitioners in the field of remote sensing who like to program and who prefer in-depth explanations, highly recommended."
-Gunter Menz,
More details
Edition
3rd New edition
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Edition type
New edition
Illustrations
20 page follows page 230, 143 s/w Abbildungen
20 page follows page 230; 143 Illustrations, black and white
Dimensions
Height: 235 mm
Width: 156 mm
Weight
953 gr
ISBN-13
978-1-4665-7037-5 (9781466570375)
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 Python, Fourth Edition
Book
03/2019
4th Edition
CRC Press
€215.77
Article exhausted; check different version
Previous edition
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
Article exhausted; check for reprint
Person
Content
Images, Arrays, and Matrices
Multispectral satellite images
Synthetic aperture radar images
Algebra of vectors and matrices
Eigenvalues and eigenvectors
Singular value decomposition
Finding minima and maxima
Exercises
Image Statistics
Random variables
Parameter estimation
Multivariate distributions
Bayes' Theorem, likelihood and classification
Hypothesis testing
Ordinary linear regression
Entropy and information
Exercises
Transformations
The discrete Fourier transform
The discrete wavelet transform
Principal components
Minimum noise fraction
Spatial correlation
Exercises
Filters, Kernels and Fields
The Convolution Theorem
Linear filters
Wavelets and filter banks
Kernel methods
Gibbs-Markov random fields
Exercises
Image Enhancement and Correction
Lookup tables and histogram functions
High-pass spatial filtering and feature extraction
Panchromatic sharpening
Radiometric correction of polarimetric SAR imagery
Topographic correction
Image-image registration
Exercises
Supervised Classification Part
Maximizing the a posteriori probability
Training data and separability
Maximum likelihood classification
Gaussian kernel classification
Neural networks
Support vector machines
Exercises
Supervised Classification Part
Postprocessing
Evaluation and comparison of classification accuracy
Adaptive boosting
Classification of polarimetric SAR imagery
Hyperspectral image analysis
Exercises
Unsupervised Classification
Simple cost functions
Algorithms that minimize the simple cost functions
Gaussian mixture clustering
Including spatial information
A benchmark
The Kohonen self-organizing map
Image segmentation
Exercises
Change Detection
Algebraic methods
Postclassification comparison
Principal components analysis (PCA)
Multivariate alteration detection (MAD)
Decision thresholds
Unsupervised change classification
Change detection with polarimetric SAR imagery
Radiometric normalization of multispectral imagery
Exercises
A Mathematical Tools
B Efficient Neural Network Training Algorithms
C ENVI Extensions in IDL
D Python Scripts
Mathematical Notation
References
Index
Multispectral satellite images
Synthetic aperture radar images
Algebra of vectors and matrices
Eigenvalues and eigenvectors
Singular value decomposition
Finding minima and maxima
Exercises
Image Statistics
Random variables
Parameter estimation
Multivariate distributions
Bayes' Theorem, likelihood and classification
Hypothesis testing
Ordinary linear regression
Entropy and information
Exercises
Transformations
The discrete Fourier transform
The discrete wavelet transform
Principal components
Minimum noise fraction
Spatial correlation
Exercises
Filters, Kernels and Fields
The Convolution Theorem
Linear filters
Wavelets and filter banks
Kernel methods
Gibbs-Markov random fields
Exercises
Image Enhancement and Correction
Lookup tables and histogram functions
High-pass spatial filtering and feature extraction
Panchromatic sharpening
Radiometric correction of polarimetric SAR imagery
Topographic correction
Image-image registration
Exercises
Supervised Classification Part
Maximizing the a posteriori probability
Training data and separability
Maximum likelihood classification
Gaussian kernel classification
Neural networks
Support vector machines
Exercises
Supervised Classification Part
Postprocessing
Evaluation and comparison of classification accuracy
Adaptive boosting
Classification of polarimetric SAR imagery
Hyperspectral image analysis
Exercises
Unsupervised Classification
Simple cost functions
Algorithms that minimize the simple cost functions
Gaussian mixture clustering
Including spatial information
A benchmark
The Kohonen self-organizing map
Image segmentation
Exercises
Change Detection
Algebraic methods
Postclassification comparison
Principal components analysis (PCA)
Multivariate alteration detection (MAD)
Decision thresholds
Unsupervised change classification
Change detection with polarimetric SAR imagery
Radiometric normalization of multispectral imagery
Exercises
A Mathematical Tools
B Efficient Neural Network Training Algorithms
C ENVI Extensions in IDL
D Python Scripts
Mathematical Notation
References
Index