Classification Methods for Remotely Sensed Data

Since the publishing of the first edition of Classification Methods for Remotely Sensed Data in 2001, the field of pattern recognition has expanded in many new directions that make use of new technologies to capture data and more powerful computers to mine and process it. What seemed visionary but a decade ago is now being put to use and refined in commercial applications as well as military ones. Keeping abreast of these new developments, Classification Methods for Remotely Sensed Data, Second Edition provides a comprehensive and up-to-date review of the entire field of classification methods applied to remotely sensed data. This second edition provides seven fully revised chapters and two new chapters covering support vector machines (SVM) and decision trees. It includes updated discussions and descriptions of Earth observation missions along with updated bibliographic references. After an introduction to the basics, the text provides a detailed discussion of different approaches to image classification, including maximum likelihood, fuzzy sets, and artificial neural networks. This cutting-edge resource: Presents a number of approaches to solving the problem of allocation of data to one of several classes Covers potential approaches to the use of decision trees Describes developments such as boosting and random forest generation Reviews lopping branches that do not contribute to the effectiveness of the decision trees Complete with detailed comparisons, experimental results, and discussions for each classification method introduced, this book will bolster the work of researchers and developers by giving them access to new developments. It also provides students with a solid foundation in remote sensing data classification methods.

Table of Contents:
Preface to the Second Edition Preface to the First Edition Author Biographies Chapter 1: Remote Sensing in the Optical and Microwave Regions 1.1 Introduction to Remote Sensing 1.1.1 Atmospheric Interactions 1.1.2 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 Effect 1.3.2.2 Steps in Atmospheric Correction 1.4 Correction for Topographic Effects 1.5 Remote Sensing in the Microwave Region 1.6 Radar Fundamentals 1.6.1 SLAR Image Resolution 1.6.2 Geometric Effects on Radar Images 1.6.3 Factors Affecting Radar Backscatter 1.6.3.1 Surface Roughness 1.6.3.2 Surface Conductivity 1.6.3.3 Parameters of the Radar Equation 1.7 Imaging Radar Polarimetry 1.7.1 Radar Polarization State 1.7.2 Polarization Synthesis 1.7.3 Polarization Signatures 1.8 Radar Speckle Suppression 1.8.1 Multilook Processing 1.8.2 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 Autocorrelation Factors (MAF) 2.1.4 Maximum Noise Fraction Transformation 2.2 Feature Selection 2.3 Fundamental Pattern Recognition Techniques 2.3.1 Unsupervised Methods 2.3.1.1 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 Classifier 2.4 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 Size 2.6.1 Sampling Scheme 2.6.2 Sample Size, Scale, and Spatial Variability 2.6.3 Adequacy of Training Data 2.7 Estimation of Classification Accuracy Epilogue Chapter 3: Artificial Neural Networks 3.1 Multilayer Perceptron 3.1.1 Back-Propagation 3.1.2 Parameter Choice, Network Architecture, and Input/Output Coding 3.1.3 Decision Boundaries in Feature Space 3.1.4 Overtraining and Network Pruning 3.2 Kohonen's Self-Organizing Feature Map 3.2.1 SOM Network Construction and Training 3.2.1.1 Unsupervised Training 3.2.1.2 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 Networks 3.4.1 Hopfield Network Structure 3.4.2 Hopfield Network Dynamics 3.4.3 Network Convergence 3.4.4 Issues Relating to Hopfield Networks 3.4.5 Energy and Weight Coding: An Example 3.5 Adaptive Resonance Theory (ART) 3.5.1 Fundamentals of the ART Model 3.5.2 Choice of Parameters 3.5.3 Fuzzy ARTMAP 3.6 Neural Networks in Remote Sensing Image Classification 3.6.1 An Overview 3.6.2 A Comparative Study Chapter 4: Support Vector Machines 4.1 Linear Classification 4.1.1 The Separable Case4.1.2 The Nonseparable Case 4.2 Nonlinear Classification and Kernel Functions 4.2.1 Nonlinear SVMs 4.2.2 Kernel Functions 4.3 Parameter Determination 4.3.1 t-fold Cross-Validations 4.3.2 Bound on Leave-One-Out Error 4.3.3 Grid Search 4.3.4 Gradient Descent Method 4.4 Multiclass Classification 4.4.1 One-against-One, One-against-Others, and DAG 4.4.2 Multiclass SVMs 4.4.2.1 Vapnik's Approach 4.4.2.2 Methodology of Crammer and Singer 4.5 Feature Selection 4.6 SVM Classification of Remotely Sensed Data 4.7 Concluding Remarks Chapter 5: Methods Based on Fuzzy Set Theory 5.1 Introduction to Fuzzy Set Theory 5.1.1 Fuzzy Sets: Definition 5.1.2 Fuzzy Set Operations 5.2 Fuzzy C-Means Clustering Algorithm 5.3 Fuzzy Maximum Likelihood Classification 5.4 Fuzzy Rule Base 5.4.1 Fuzzification 5.4.2 Inference 5.4.3 Defuzzification 5.5 Image Classification Using Fuzzy Rules 5.5.1 Introductory Methodology 5.5.2 Experimental Results Chapter 6: Decision Trees 6.1 Feature Selection Measures for Tree Induction 6.1.1 Information Gain 6.1.2 Gini Impurity Index 6.2 ID3, C4.5, and SEE5.0 Decision Trees 6.2.1 ID3 6.2.2 C4.5 6.2.3 SEE5.0 6.3 CHAID 6.4 CART 6.5 QUEST 6.5.1 Split Point Selection 6.5.2 Attribute Selection 6.6 Tree Induction from Artificial Neural Networks 6.7 Pruning Decision Trees 6.7.1 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 Forest 6.8.1 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 Dimensions 7.1.1 Introduction to Fractals 7.1.2 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 Matrix 7.6.2.5 Multiplicative Autoregressive Random Field 7.6.3 Segmentation Results 7.6.4 Texture Measure of Remote Sensing Patterns Chapter 8: Modeling Context Using Markov Random Fields 8.1 Markov Random Fields and Gibbs Random Fields 8.1.1 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 Estimations 8.4 MAP-MRF Classification Algorithms 8.4.1 Iterated Conditional Modes 8.4.2 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 the Spectral Domain 9.1.3 Performance Overview of Fusion Methods 9.2 Multisource Classification Using the Stacked-Vector Method 9.3 The Extension of Bayesian Classification Theory 9.3.1 An Overview 9.3.1.1 Feature Extraction 9.3.1.2 Probability or Evidence Generation 9.3.1.3 Multisource Consensus 9.3.2 Bayesian Multisource Classification Mechanism 9.3.3 A Refined Multisource Bayesian Model 9.3.4 Multisource Classification Using the Markov Random Field 9.3.5 Assumption of Intersource Independence 9.4 Evidential Reasoning 9.4.1 Concept Development 9.4.2 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 Bibliography Index