Classification Methods for Remotely Sensed Data, Second Edition

Content: Preface to the Second EditionPreface to the First EditionAuthor BiographiesChapter 1: Remote Sensing in the Optical and Microwave Regions1.1 Introduction to Remote Sensing 1.1.1 Atmospheric Interactions1.1.2 Surface Material Reflectance 1.1.3 Spatial and Radiometric Resolution 1.2 Optical Remote Sensing Systems 1.3 Atmospheric Correction 1.3.1 Dark Object Subtraction 1.3.2 Modeling Techniques 1.3.2.1 Modeling the Atmospheric Effect1.3.2.2 Steps in Atmospheric Correction1.4 Correction for Topographic Effects 1.5 Remote Sensing in the Microwave Region 1.6 Radar Fundamentals 1.6.1 SLAR Image Resolution1.6.2 Geometric Effects on Radar Images1.6.3 Factors Affecting Radar Backscatter1.6.3.1 Surface Roughness 1.6.3.2 Surface Conductivity1.6.3.3 Parameters of the Radar Equation1.7 Imaging Radar Polarimetry1.7.1 Radar Polarization State1.7.2 Polarization Synthesis 1.7.3 Polarization Signatures1.8 Radar Speckle Suppression 1.8.1 Multilook Processing1.8.2 Filters for Speckle Suppression Chapter 2: Pattern Recognition Principles 2.1 Feature Space Manipulation 2.1.1 Tasseled Cap Transform 2.1.2 Principal Components Analysis 2.1.3 Minimum/Maximum AutocorrelationFactors (MAF) 2.1.4 Maximum Noise Fraction Transformation 2.2 Feature Selection 2.3 Fundamental Pattern Recognition Techniques 2.3.1 Unsupervised Methods2.3.1.1 The k-means Algorithm 2.3.1.2 Fuzzy Clustering 2.3.2 Supervised Methods 2.3.2.1 Parallelepiped Method 2.3.2.2 Minimum Distance Classifier 2.3.2.3 Maximum Likelihood Classifier2.4 Combining Classifiers 2.5 Incorporation of Ancillary Information 2.5.1 Use of Texture and Context 2.5.2 Using Ancillary Multisource Data 2.6 Sampling Scheme and Sample Size2.6.1 Sampling Scheme2.6.2 Sample Size, Scale, and Spatial Variability 2.6.3 Adequacy of Training Data 2.7 Estimation of Classification AccuracyEpilogue Chapter 3: Artificial Neural Networks3.1 Multilayer Perceptron3.1.1 Back-Propagation 3.1.2 Parameter Choice, Network Architecture, andInput/Output Coding 3.1.3 Decision Boundaries in Feature Space 3.1.4 Overtraining and Network Pruning3.2 Kohonen\'s Self-Organizing Feature Map3.2.1 SOM Network Construction and Training3.2.1.1 Unsupervised Training3.2.1.2 Supervised Training 3.2.2 Examples of Self-Organization 3.3 Counter-Propagation Networks 3.3.1 Counter-Propagation Network Training 3.3.2 Training Issues 3.4 Hopfield Networks3.4.1 Hopfield Network Structure 3.4.2 Hopfield Network Dynamics 3.4.3 Network Convergence 3.4.4 Issues Relating to Hopfield Networks3.4.5 Energy and Weight Coding: An Example3.5 Adaptive Resonance Theory (ART) 3.5.1 Fundamentals of the ART Model3.5.2 Choice of Parameters3.5.3 Fuzzy ARTMAP3.6 Neural Networks in Remote Sensing Image Classification3.6.1 An Overview3.6.2 A Comparative StudyChapter 4: Support Vector Machines4.1 Linear Classification 4.1.1 The Separable Case4.1.2 The Nonseparable Case4.2 Nonlinear Classification and Kernel Functions4.2.1 Nonlinear SVMs4.2.2 Kernel Functions4.3 Parameter Determination4.3.1 t-fold Cross-Validations4.3.2 Bound on Leave-One-Out Error4.3.3 Grid Search4.3.4 Gradient Descent Method4.4 Multiclass Classification4.4.1 One-against-One, One-against-Others, and DAG4.4.2 Multiclass SVMs4.4.2.1 Vapnik\'s Approach4.4.2.2 Methodology of Crammer and Singer4.5 Feature Selection4.6 SVM Classification of Remotely Sensed Data4.7 Concluding RemarksChapter 5: Methods Based on Fuzzy Set Theory5.1 Introduction to Fuzzy Set Theory5.1.1 Fuzzy Sets: Definition5.1.2 Fuzzy Set Operations5.2 Fuzzy C-Means Clustering Algorithm5.3 Fuzzy Maximum Likelihood Classification5.4 Fuzzy Rule Base5.4.1 Fuzzification5.4.2 Inference5.4.3 Defuzzification5.5 Image Classification Using Fuzzy Rules5.5.1 Introductory Methodology5.5.2 Experimental ResultsChapter 6: Decision Trees6.1 Feature Selection Measures for Tree Induction6.1.1 Information Gain6.1.2 Gini Impurity Index6.2 ID3, C4.5, and SEE5.0 Decision Trees6.2.1 ID36.2.2 C4.56.2.3 SEE5.06.3 CHAID6.4 CART6.5 QUEST6.5.1 Split Point Selection6.5.2 Attribute Selection6.6 Tree Induction from Artificial Neural Networks6.7 Pruning Decision Trees6.7.1 Reduced Error Pruning (REP)6.7.2 Pessimistic Error Pruning (PEP) 6.7.3 Error-Based Pruning (EBP)6.7.4 Cost Complexity Pruning (CCP)6.7.5 Minimal Error Pruning (MEP) 6.8 Boosting and Random Forest6.8.1 Boosting 6.8.2 Random Forest 6.9 Decision Trees in Remotely Sensed Data Classification 6.10 Concluding Remarks Chapter 7: Texture Quantization 7.1 Fractal Dimensions7.1.1 Introduction to Fractals7.1.2 Estimation of the Fractal Dimension 7.1.2.1 Fractal Brownian Motion (FBM) 7.1.2.2 Box-Counting Methods and Multifractal Dimension 7.2 Frequency Domain Filtering 7.2.1 Fourier Power Spectrum 7.2.2 Wavelet Transform 7.3 Gray-Level Co-occurrence Matrix (GLCM) 7.3.1 Introduction to the GLCM 7.3.2 Texture Features Derived from the GLCM 7.4 Multiplicative Autoregressive Random Fields 7.4.1 MAR Model: Definition 7.4.2 Estimation of the Parameters of the MAR Model 7.5 The Semivariogram and Window Size Determination 7.6 Experimental Analysis 7.6.1 Test Image Generation 7.6.2 Choice of Texture Features 7.6.2.1 Multifractal Dimension 7.6.2.2 Fourier Power Spectrum 7.6.2.3 Wavelet Transform 7.6.2.4 Gray-Level Co-occurrence Matrix7.6.2.5 Multiplicative Autoregressive Random Field7.6.3 Segmentation Results7.6.4 Texture Measure of Remote Sensing Patterns Chapter 8: Modeling Context Using Markov Random Fields 8.1 Markov Random Fields and Gibbs Random Fields8.1.1 Markov Random Fields 8.1.2 Gibbs Random Fields 8.1.3 MRF-GRF Equivalence 8.1.4 Simplified Form of MRF 8.1.5 Generation of Texture Patterns Using MRF 8.2 Posterior Energy for Image Classification 8.3 Parameter Estimation 8.3.1 Least Squares Fit Method 8.3.2 Results of Parameter Estimations8.4 MAP-MRF Classification Algorithms 8.4.1 Iterated Conditional Modes8.4.2 Simulated Annealing 8.4.3 Maximizer of Posterior Marginals 8.5 Experimental Results Chapter 9: Multisource Classification 9.1 Image Fusion 9.1.1 Image Fusion Methods 9.1.2 Assessment of Fused Image Quality in theSpectral Domain 9.1.3 Performance Overview of Fusion Methods 9.2 Multisource Classification Using the Stacked-VectorMethod9.3 The Extension of Bayesian Classification Theory9.3.1 An Overview9.3.1.1 Feature Extraction9.3.1.2 Probability or Evidence Generation9.3.1.3 Multisource Consensus9.3.2 Bayesian Multisource Classification Mechanism9.3.3 A Refined Multisource Bayesian Model9.3.4 Multisource Classification Using the MarkovRandom Field 9.3.5 Assumption of Intersource Independence 9.4 Evidential Reasoning9.4.1 Concept Development9.4.2 Belief Function and Belief Interval 9.4.3 Evidence Combination 9.4.4 Decision Rules for Evidential Reasoning 9.5 Dealing with Source Reliability 9.5.1 Using Classification Accuracy 9.5.2 Use of Class Separability 9.5.3 Data Information Class Correspondence Matrix 9.5.4 The Genetic Algorithm 9.6 Experimental Results BibliographyIndex