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Book Cover
Book
Author Varshney, Pramod K.

Title Advanced image processing techniques for remotely sensed hyperspectral data / P. K. Varshney, M. K. Arora
Published Berlin ; [Great Britain] : Springer, [2004]
©2004

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Location Call no. Vol. Availability
 MELB  621.3678 Var/Aip  AVAILABLE
Description xv, 322 pages : illustrations (some color) ; 25 cm
Series Springer Nature Book Archives Millennium (2000-2004)
Contents Machine derived contents note: Part I General -- 1 Hyperspectral Sensors and Applications 11 -- 1.1 Introduction 11 -- 1.2 Multi-spectral Scanning Systems (MSS) 11 -- 1.3 Hyperspectral Systems 14 -- 1.3.1 Airborne sensors 14 -- 1.3.2 Spaceborne sensors 23 -- 1.4 Ground Spectroscopy 27 -- 1.5 Software for Hyperspectral Processing 29 -- 1.6 Applications 30 -- 1.6.1 Atmosphere and Hydrosphere 30 -- 1.6.2 Vegetation 33 -- 1.6.3 Soils and Geology 38 -- 1.6.4 Environmental Hazards and Anthropogenic Activity 39 -- 1.7 Summary 40 -- 2 Overview of Image Processing 51 -- 2.1 Introduction 51 -- 2.2 Image File Formats 52 -- 2.3 Image Distortion and Rectification 53 -- 2.3.1 Radiometric Distortion 53 -- 2.3.2 Geometric Distortion and Rectification 54 -- 2.4 Image Registration 56 -- 2.5 Image Enhancement 57 -- 2.5.1 Point Operations 57 -- 2.5.2 Geometric Operations 63 -- 2.6 Image Classification 66 -- 2.6.1 Supervised Classification 67 -- 2.6.2 Unsupervised Classification 69 -- 2.6.3 Crisp Classification Algorithms 71 -- 2.6.4 Fuzzy Classification Algorithms 74 -- 2.6.5 Classification Accuracy Assessment 76 -- 2.7 Image Change Detection 79 -- 2.8 Image Fusion 80 -- 2.9 Automatic Target Recognition 81 -- 2.10 Summary 82 -- Part II Theory -- 3 Mutual Information: -- A Similarity Measure for Intensity Based Image Registration 89 -- 3.1 Introduction 89 -- 3.2 Mutual Information Similarity Measure 90 -- 3.3 Joint Histogram Estimation Methods 93 -- 3.3.1 Two-Step Joint Histogram Estimation 93 -- 3.3.2 One-Step Joint Histogram Estimation 94 -- 3.4 Interpolation Induced Artifacts 95 -- 3.5 Generalized Partial Volume Estimation of Joint Histograms 99 -- 3.6 Optimization Issues in the Maximization of MI 103 -- 3.7 Summ ary 107 -- 4 Independent Component Analysis 109 -- 4.1 Introduction 109 -- 4.2 Concept of ICA 109 -- 4.3 ICA Algorithms 113 -- 4.3.1 Preprocessing using PCA 113 -- 4.3.2 Information Minimization Solution for ICA 115 -- 4.3.3 ICA Solution through Non-Gaussianity Maximization 121 -- 4.4 Application of ICA to Hyperspectral Imagery 123 -- 4.4.1 Feature Extraction Based Model 124 -- 4.4.2 Linear Mixture Model Based Model 125 -- 4.4.3 An ICA algorithm for Hyperspectral Image Processing 126 -- 4.5 Summary 129 -- 5 Support Vector Machines 133 -- 5.1 Introduction 133 -- 5.2 Statistical Learning Theory 135 -- 5.2.1 Empirical Risk Minimization 136 -- 5.2.2 Structural Risk Minimization 137 -- 5.3 Design of Support Vector Machines 138 -- 5.3.1 Linearly Separable Case 139 -- 5.3.2 Linearly Non-Separable Case 143 -- 5.3.3 Non-Linear Support Vector Machines 146 -- 5.4 SVMs for Multiclass Classification 148 -- 5.4.1 One Against the Rest Classification 149 -- 5.4.2 Pairwise Classification 149 -- 5.4.3 Classification based on Decision Directed Acyclic Graph -- and Decision Tree Structure 150 -- 5.4.4 Multiclass Objective Function 152 -- 5.5 Optimization Methods 152 -- 5.6 Summary 154 -- 6 Markov Random Field Models 159 -- 6.1 Introduction 159 -- 6.2 MRF and Gibbs Distribution 161 -- 6.2.1 Random Field and Neighborhood 161 -- 6.2.2 Cliques, Potential and Gibbs Distributions 162 -- 6.3 MRF Modeling in Remote Sensing Applications 165 -- 6.4 Optimization Algorithms 167 -- 6.4.1 Simulated Annealing 168 -- 6.4.2 Metropolis Algorithm 173 -- 6.4.3 Iterated Conditional Modes Algorithm 175 -- 6.5 Summary 177 -- Part III Applications -- 7 MI Based Registration of Multi-Sensor and Multi-Temporal Images 181 -- 7.1 Introduction 181 -- 7.2 Registration Consistency 183 -- 7.3 Multi-Sensor Registration 184 -- 7.3.1 Registration of Images -- Having a Large Difference in Spatial Resolution 184 -- 7.3.2 Registration of Images -- Having Similar Spatial Resolutions 188 -- 7.4 Multi-Temporal Registration 190 -- 7.5 Summary 197 -- 8 Feature Extraction from Hyperspectral Data Using ICA 199 -- 8.1 Introduction 199 -- 8.2 PCA vs ICA for Feature Extraction 200 -- 8.3 Independent Component Analysis Based -- Feature Extraction Algorithm (ICA-FE) 202 -- 8.4 Undercomplete Independent Component Analysis Based -- Feature Extraction Algorithm (UICA-FE) 203 -- 8.5 Experimental Results 210 -- 8.6 Summary 215 -- 9 Hyperspectral Classification Using ICA Based Mixture Model 217 -- 9.1 Introduction 217 -- 9.2 Independent Component Analysis Mixture Model (ICAMM) - -- Theory 219 -- 9.2.1 ICAMM Classification Algorithm 220 -- 9.3 Experimental Methodology 222 -- 9.3.1 Feature Extraction Techniques 223 -- 9.3.2 Feature Ranking 224 -- 9.3.3 Feature Selection 224 -- 9.3.4 Unsupervised Classification 225 -- 9.4 Experimental Results and Analysis 225 -- 9.5 Summary 233 -- 10 Support Vector Machines for Classification -- of Multi- and Hyperspectral Data 237 -- 10.1 Introduction 237 -- 10.2 Parameters Affecting SVM Based Classification 239 -- 10.3 Remote Sensing Images 241 -- 10.3.1 Multispectral Image 241 -- 10.3.2 Hyperspectral Image 242 -- 10.4 SVM Based Classification Experiments 243 -- 10.4.1 Multiclass Classification 243 -- 10.4.2 Choice of Optimizer 245 -- 10.4.3 Effect of Kernel Functions 248 -- 10.5 Summary 254 -- 11 An MRF Model Based Approach -- for Sub-pixel Mapping from Hyperspectral Data 257 -- 11.1 Introduction 257 -- 11.2 MRF Model for Sub-pixel Mapping 259 -- 11.3 Optimum Sub-pixel Mapping Classifier 261 -- 11.4 Experimental Results 265 -- 11.4.1 Experiment 1: -- Sub-pixel Mapping from Multispectral Data 266 -- 11.4.2 Experiment 2: -- Sub-pixel Mapping from Hyperspectral Data 271 -- 11.5 Summary 276 -- 12 Image Change Detection and Fusion Using MRF Models 279 -- 12.1 Introduction 279 -- 12.2 Image Change Detection using an MRF model 279 -- 12.2.1 Image Change Detection (ICD) Algorithm 281 -- 12.2.2 Optimum Detector 284 -- 12.3 Illustrative Examples of Image Change Detection 285 -- 12.3.1 Example 1: Synthetic Data 287 -- 12.3.2 Example 2: Multispectral Remote Sensing Data 290 -- 12.4 Image Fusion using an MRF model 292 -- 12.4.1 Image Fusion Algorithm 294 -- 12.5 Illustrative Examples of Image Fusion 299 -- 12.5.1 Example 1: Multispectral Image Fusion 299 -- 12.5.2 Example 2: Hyperspectral Image Fusion 303 -- 12.6 Summary 306
Bibliography Includes bibliographical references and index
Subject Image processing -- Digital techniques.
Multispectral imaging.
Optical data processing -- Technique.
Remote sensing.
Author Arora, M. K. (Manoj K.), 1963-
LC no. 2004104167
ISBN 3540216685 :