Recommended books-image analysis, classification, and change detection in remote sensing with Algorithms

Features

·    Brief Introduction to the necessary mathematical and statistical background knowledge

·    In-depth introduction to non-linear data analysis methods, including Support Vector Machines

·    Detailed introduction of variable change detection and software implementation

·    Provides the exercise source code for each chapter

·    The author's personal website updates the latest ENVI Secondary Development Program at any time

Library directory:

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
Glas- 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
Demo-thresholds and unsupervised classification of changes
Radiometric Normalization

Appendix:
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

    Book details: http://www.crcpress.com/product/isbn/9781420087130

   Available in Amazon.

 

Library directory:

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
Glas- 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
Demo-thresholds and unsupervised classification of changes
Radiometric Normalization

Appendix:
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

    Book details: http://www.crcpress.com/product/isbn/9781420087130

   Available in Amazon.