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

Out of Print

By: Morton J Canty

CRC Press

Hardback | Jan 2010 | Edition: 2 | #185456 | ISBN-13: 9781420087130
Out of Print Details

About this book

Introducing advanced undergraduate and graduate level techniques used in the processing of multispectral and hyperspectral remote sensing imagery, Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL covers such topics as basic Fourier, wavelet, principle components, minimum noise fraction transformation, and Ortho-rectification; panchromatic sharpening; multivariate change detection; supervised and unsupervised land cover classification; hyperspectral analysis; and topographic modeling. With programming examples in IDL and applications that support ENVI, it offers extensions for data fusion, statistical change detection, clustering and supervised classification with neural networks, all available as source code.


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

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
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
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

Appendix D: Mathematical Notation



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