Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL - Rilegato

Canty, Morton J.

Rilegato

ISBN 10: 0849372518 ISBN 13: 9780849372513

Casa editrice: CRC Press, 2006

Riassunto

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.

Su questo libro

Contenuti

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

Dettagli dell?edizione

• Editore: CRC Press
• Data di pubblicazione: 2006
• ISBN 10: 0849372518
• ISBN 13: 9780849372513
• Rilegatura: Copertina rigida
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 368