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.
Contents
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
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