圖書推薦——Image Analysis, Classification, and Change Detection in Remote Sensing With Algorithms

圖書特點

·      簡明介紹所需的數學和統計背景知識

·      深度介紹非線性資料分析方法，包括支援向量機等

·      詳細介紹多變量變化檢測及軟體的實作

·      提供每個章節的練習源代碼

·      作者個人網站随時更新最新的ENVI二次開發程式

圖書目錄：

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

     圖書詳細介紹：http://www.crcpress.com/product/isbn/9781420087130

    在Amazon可購買。

圖書目錄：

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

     圖書詳細介紹：http://www.crcpress.com/product/isbn/9781420087130

    在Amazon可購買。