图书推荐——Image Analysis, Classification, and Change Detection in Remote Sensing With Algorithms
图书特点
· 简明介绍所需的数学和统计背景知识
· 深度介绍非线性数据分析方法，包括支持向量机等
· 详细介绍多变量变化检测及软件的实现
· 提供每个章节的练习源代码
· 作者个人网站随时更新最新的ENVI二次开发程序
图书目录：
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
图书详细介绍：http://www.crcpress.com/product/isbn/9781420087130
在Amazon可购买。
图书目录：
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
图书详细介绍：http://www.crcpress.com/product/isbn/9781420087130
在Amazon可购买。