Classification methods for remotely sensed data:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
2025
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxi, 421 Seiten Illustrationen, Diagramme |
| ISBN: | 9781032573939 9781032573953 |
Internformat
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Datensatz im Suchindex
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Contents Preface to the Third Edition.xiii Preface to the Second Edition. xv Preface to the First Edition. xvi Acknowledgments. xx Authors. xxi Chapter 1 Fundamentals of Remote Sensing. 1 Introduction to Remote Sensing. 3 1.1.1 Atmospheric Interactions.4 1.1.2 Reflectance Properties of Surface Materials. 5 1,1.3 Spatial, Spectral, and Radiometric Resolution. 8 1.1.4 Scale Issues in Remote Sensing. 10 1.2 Optical Remote Sensing Systems. 12 1.3 Atmospheric Correction. 13
1.3.1 Dark Object Subtraction. 14 1.3.2 Modeling Techniques. 15 1.3.2.1 Modeling the Atmospheric Effect. 15 1.3.2.2 Steps in Atmospheric Correction. 18 1.4 Correction for Topographic Effects. 20 1.5 Remote Sensing in the Microwave Region. 23 1.6 Radar Fundamentals. 23 1.6.1 SLAR Image Resolution. 24 1.6.2 Geometric Effects on Radar Images.26 1.6.3 Factors Affecting Radar Backscatter. 29 1.6.3.1 Surface Roughness. 29 1.6.3.2 Surface Conductivity. 30 1.6.3.3 Parameters of the Radar Equation. 30 1.7 Imaging Radar Polarimetry.31 1.7.1 Radar Polarization State. 32 1.7.2 Polarization Synthesis.
34 1.7.3 Polarization Signatures. 35 1.8 Radar Speckle Suppression. 37 1.8.1 Multilook Processing. 37 1.8.2 Filters for Speckle Suppression. 38 References. 41 1.1 Chapter 2 Pattern Recognition Principles.47 2.1 2.2 2.3 A Terminological Introduction. 49 Taxonomy of Classification Techniques.50 Fundamental Pattern Recognition Techniques. 52 2.3.1 Unsupervised Methods. 52 2.3.1.1 The A--Means Algorithm. 53 2.3.1.2 Fuzzy C-Means Clustering. 54 2.3.2 Supervised Methods. 58 2.3.2.1 Parallelepiped Method.59 vii
Contents viii 2.3.2.2 Minimum Distance Classifier.59 2.3.2.3 Maximum Likelihood Classifier. 60 2.3.2.4 Fuzzy Maximum Likelihood Classifier. 62 2.4 Spectral Unmixing. 64 2.5 Ensemble Classifiers. 66 2.6 Incorporation of Ancillary Information. 67 2.6.1 Use of Texture and Context.68 2.6.2 Using Ancillary Multisource Data. 72 2.7 Epilogue. 73 References. 74 Chapter 3 Dimensionality Reduction:Feature Extraction and Selection. 82 3.1 Feature Extraction.85 3.1.1 Principal Component Analysis. 85 3.1.2 Minimum/Maximum Autocorrelation Factors. 89 3.1.3 Maximum Noise Fraction (MNF) Transformation. 90 3.1.4 Independent
Component Analysis. 91 3.1.5 Projection Pursuit. 91 3.2 Feature Selection. 92 3.2.1 Greedy Search Methods.93 3.2.2 Simulated Annealing. 96 3.2.3 Separability Indices. 98 3.2.4 Filter-Based Methods.99 3.2.4.1 Correlation-Based Feature Selection. 99 3.2.4.2 Information Gain. 101 3.2.4.3 Gini Impurity Index. 102 3.2.4.4 Minimum Redundancy-Maximum Relevance. 102 3.2.4.5 Chi-Square Test. 103 3.2.4.6 Relief-F. 104 3.2.4.7 Symmetric Uncertainty. 104 3.2.4.8 Fisher’s Test. 105 3.2.4.9 OneR. 105 3.2.5
Wrappers.106 3.2.5.1 Genetic Algorithm.106 3.2.5.2 Particle Swarm Optimization. 109 3.2.5.3 Feature Selection with SVMs. 110 3.2.6 Embedded Methods. 111 3.2.6.1 К-Nearest Neighbor-Based Feature Selection. 112 3.2.6.2 Feature Selection with Ensemble Learners. 112 3.2.6.3 Hilbert-Schmidt Independence Criterion withLasso. 113 3.3 Concluding Remarks. 114 References. 116 Chapter 4 Multisource Image Fusion and Classification. 124 4.1 Image Fusion. 125 4.1.1 Image Fusion Methods. 127 4.1.1.1 PCA-Based Image Fusion.127 4.1.1.2 IHS-Based Image Fusion. 127 4.1.1.3 Brovey Transform.129
Contents ίχ 4.1.1.4 Gram-Schmidt Transform. 130 4.1.1.5 Wavelet Transform. 131 4.1.1.6 Deep Learning for Image Fusion. 135 4.1.2 Assessment of Fused Image Quality. 138 4.1.3 Performance Evaluation of Fusion Methods. 140 4.2 Multisource Classification Using the Stacked-Vector Method. 142 4.3 The Extension of Bayesian Classification Theory. 143 4.3.1 An Overview. 143 4.3.1.1 Feature Extraction.143 4.3.1.2 Probability or Evidence Generation. 144 4.3.1.3 Multisource Consensus. 144 4.3.2 Bayesian Multisource Classification Mechanism. 145 4.3.3 A Refined Multisource Bayesian Model. 146 4.3.4 Multisource Classification Using the MRF. 147 4.3.5 Assumption of Inter-Source Independence. 148 4.4 Evidential Reasoning. 148 4.4.1 Concept
Development. 148 4.4.2 Belief Function and Belief Interval. 150 4.4.3 Evidence Combination. 152 4.4.4 Decision Rules for Evidential Reasoning. 154 4.5 Dealing with Source Reliability. 154 4.5.1 Using Classification Accuracy.155 4.5.2 Use of Class Separability. 155 4.5.3 Data Information Class Correspondence Matrix. 155 4.6 Concluding Remarks and Future Trends. 157 References. 159 Chapter 5 Support Vector Machines. 165 5.1 Linear Classification. 168 5.1.1 The Separable Case.168 5.1.2 The Nonseparable Case.170 5.2 Nonlinear Classification and KernelFunctions. 171 5.2.1 Nonlinear
SVMs. 171 5.2.2 Kernel Functions. 173 5.3 Parameter Determination. 174 5.3.1 -Fold Cross-Validations.174 5.3.2 Bound on Leave-One-Out Error. 175 5.3.3 Grid Search. 177 5.3.4 Gradient Descent Method.177 5.4 Multiclass Classification. 179 5.4.1 One-against-One, Onc-against-Others, and DAG. 179 5.4.2 Multiclass SVMs. 180 5.4.2.1 Vapnik’s Approach.180 5.4.2.2 Methodology of Crammer and Singer. 181 5.5 Relevance Vector Machines. 182 5.6 Twin Support Vector Machines. 184 5.7 Deep Support Vector Machines. 186 5.8 Concluding
Remarks. 190 References. 191
Contents X Chapter 6 Decision Trees. 197 ID3, C4.5, and SEE5.0 Decision Trees. 199 6.1.1 ID3. 199 6.1.2 C4.5.203 6.1.3 SEE5.0 (C5.0). 205 6.2 CHAID. 206 6.3 CART.207 6,4 QUEST. 210 6.4.1 Split Point Selection. 210 6.4.2 Attribute Selection. 211 6.5 Tree Induction from Artificial Neural Networks. 213 6.6 Pruning Decision Trees.213 6.6.1 Reduced Error Pruning. 214 6.6.2 Pessimistic Error
Pruning. 215 6.6.3 Error-Based Pruning. 216 6.6.4 Cost Complexity Pruning. 216 6.6.5 Minimal Error Pruning. 219 6.7 Ensemble Methods. 220 6.7.1 Boosting. 220 6.7.2 Random Forest. 222 6.7.3 Rotation Forest.224 6.7.4 Canonical Correlation Forest. 227 6.7.5 Extreme Gradient Boosting. 228 6.7.6 Light Gradient Boosting Machines. 230 6.7.7 Gradient Boosting Machines. 231 6.7.8 Categorical Boosting. 232 6.7.9 Natural Gradient Boosting.233 6.8 Concluding Remarks.233
References. 235 6.1 Chapter 7 Deep Learning. 240 7.1 7.2 Fundamentals. 242 7.1.1 Stochastic Gradient Descent. 242 7.1.2 Backpropagation. 242 7.1.3 Regularization. 245 7.1.3.1 Weight Decay. 246 7.1.3.2 Dropout. 247 7.1.3.3 Data Augmentation. 249 7.1.3.4 Early Stopping. 250 7.1.4 Activation Functions. 251 7.1.5 Loss Functions. 254 Neural Network Architectures. 256 7.2.1 Multilayer Perceptron.257 7.2.2 Convolutional Neural
Networks. 258 7.2.2.1 Convolutional Layers.259 7.2.2.2 Pooling Layers. 262 7.2.2.3 Fully ConnectedLayers. 262 7.2.2.4 Receptive Fieldand Feature Map. 263 7.2.2.5 Training CNNs. 264
x» Contents 7.2.2.6 Data Structures inCNNs. 265 7.2.2.7 Evolving Trends in CNN Design. 265 7.2.3 Recurrent Neural Networks. 267 7.2.3.1 Long- and Short-Term Memory. 268 7.2.3.2 Gated Recurrent Unit. 269 7.2.4 Vision Transformers. 270 7.2.5 Deep Multilayer Perceptron. 271 7.2.6 Generative Adversarial Networks. 274 7.2.7 Deep Autoencoders. 275 7.2.7.1 Undercomplete Autoencoders. 277 7.2.7.2 Regularized Autoencoders. 278 7.2.7.3 Sparse Autoencoders. 278 7.2.74 Denoising Autoencoders. 279 7.2.7.5 Variational Autoencoders.280 7.3 Learning Paradigms.281 7.3.1 Transfer Learning.282 7.3.2 Semi-Supervised
Learning. 284 7.3.3 Reinforcement Learning. 286 7.3.4 Active Learning. 288 7.3.5 Multitask Learning. 289 7.4 Application of DL in Remote Sensing. 290 7,4.1 Semantic Segmentation.290 7.4.2 Object Detection. 292 7.4.3 Scene Classification. 294 7.4,4 Change Detection. 296 7.5 Concluding Remarks.297 References. 300 Chapter 8 Object-Based Image Analysis. 311 8.1 Clustering-Based Segmentation. 314 8.1.1 Mean-Shift Algorithm. 315 8.1.2 Superpixel
Segmentation. 317 8.2 Thresholding-Based Segmentation. 319 8.3 Edge-Based Segmentation. 319 8.4 Watershed Segmentation. 320 8.5 Region-Based Segmentation. 321 8.5.1 Region Splitting and Merging.322 8.5.2 Region Growing. 322 8.5.3 Multiresolution Segmentation.323 8.6 Hybrid Segmentation.326 8.7 Evaluation of Segmentation Quality. 327 8.7.1 Supervised Approach. 328 8.7.2 Unsupervised Approach. 331 8.7.2.1 Estimation of the Seale Parameter. 331 8.7.2.2 Global Score. 333 8.7.2.3 Overall Goodness F-Measure. 334 8.8 Concluding
Remarks. 335 References. 335
Contents xii Chapter 9 Hyperparameter Optimization. 342 What Is Hyperparameter Optimization?. 344 Hyperparameter Optimization Techniques. 347 9.2.1 Model-Free Algorithms. 347 9.2.1.1 Trial-and-Error (ManualTesting). 347 9.2.1.2 Grid Search. 348 9.2.1.3 Random Search. 349 9.2,2 Gradient-Based Optimization.350 9.2.3 Bayesian Optimization. 351 9.2.4 Multifidelity Optimization. 353 9.2.4.1 Successive Halving. 353 9.2.4.2 Hyperband. 354 9.2.5 Metaheuristic Algorithms. 355 9.2.5.1 Genetic Algorithm. 355 9.2.5.2 Particle Swarm Optimization. 357 9.3 Challenges in Hyperparameter
Optimization. 358 9.4 Concluding Remarks. 359 References.360 9.1 9.2 Chapter 10 Accuracy Assessment and Model Explainability. 363 Accuracy Assessment. 365 10.1.1 Sampling Scheme and Spatial Autocorrelation. 367 10.1.2 Sample Size, Scale, and Spatial Variability. 369 10.1.3 Adequacy of Training and Testing Data.372 10.1.4 Conventional Accuracy Analysis. 374 10.1.5 Accuracy Analysis for Machine Learning. 378 10.1,6 Fuzzy Accuracy Assessment. 381 10.1.7 Object-Based Accuracy Assessment. 382 10.2 Comparison of Thematic Maps. 384 10.2.1 McNemar’s Test. 386 10.2.2 z-Test. 387 10.2.3 Wilcoxon Signed-Ranks
Test. 387 10.2.4 5x2-Cross-Validation r-Test. 388 10.2.5 Friedman Test.388 10.3 Explainability Methods. 389 10.3.1 SHapley Additive exPlanations. 391 10.3.2 Partial Dependence Plot.392 10.3.3 Pairwise Interaction Importance. 392 10.3.4 Permutation-Based Feature Importance. 393 10.3.5 Local Interpretable Model-Agnostic Explanations (LIME). 393 10.4 A Case Study for Accuracy Assessment and X AI. 394 10.5 Conclusions and Guidelines for Best Practice. 403 References. 406 10.1 Index. 417 |
| any_adam_object | 1 |
| author | Kavzoglu, Taskin Tso, Brandt Mather, Paul M. 1944-2021 |
| author_GND | (DE-588)135046841X (DE-588)1158709021 (DE-588)1049155378 |
| author_facet | Kavzoglu, Taskin Tso, Brandt Mather, Paul M. 1944-2021 |
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| bvnumber | BV049936391 |
| classification_rvk | RB 10232 |
| ctrlnum | (OCoLC)1460284324 (DE-599)OBVAC17295590 |
| discipline | Geographie |
| edition | Third edition |
| format | Book |
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| illustrated | Illustrated |
| indexdate | 2025-01-28T09:07:38Z |
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| isbn | 9781032573939 9781032573953 |
| language | English |
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| physical | xxi, 421 Seiten Illustrationen, Diagramme |
| publishDate | 2025 |
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| publishDateSort | 2025 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Kavzoglu, Taskin Verfasser (DE-588)135046841X aut Classification methods for remotely sensed data Taskin Kavzoglu, Brandt Tso, and Paul M. Mather Third edition Boca Raton ; London ; New York CRC Press 2025 © 2025 xxi, 421 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Soft Computing (DE-588)4455833-8 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf Satellitenbildauswertung (DE-588)4116326-6 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Fernerkundung (DE-588)4016796-3 gnd rswk-swf Klassifikation (DE-588)4030958-7 gnd rswk-swf Fernerkundung (DE-588)4016796-3 s Fuzzy-Logik (DE-588)4341284-1 s Klassifikation (DE-588)4030958-7 s Methode (DE-588)4038971-6 s Mustererkennung (DE-588)4040936-3 s DE-604 Satellitenbildauswertung (DE-588)4116326-6 s Soft Computing (DE-588)4455833-8 s Tso, Brandt Verfasser (DE-588)1158709021 aut Mather, Paul M. 1944-2021 Verfasser (DE-588)1049155378 aut Erscheint auch als Online-Ausgabe 978-1-003-43917-2 BV049943309 Erscheint auch als Online-Ausgabe, EPUB 9781040099117 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035274704&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kavzoglu, Taskin Tso, Brandt Mather, Paul M. 1944-2021 Classification methods for remotely sensed data Soft Computing (DE-588)4455833-8 gnd Methode (DE-588)4038971-6 gnd Satellitenbildauswertung (DE-588)4116326-6 gnd Mustererkennung (DE-588)4040936-3 gnd Fuzzy-Logik (DE-588)4341284-1 gnd Fernerkundung (DE-588)4016796-3 gnd Klassifikation (DE-588)4030958-7 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)4038971-6 (DE-588)4116326-6 (DE-588)4040936-3 (DE-588)4341284-1 (DE-588)4016796-3 (DE-588)4030958-7 |
| title | Classification methods for remotely sensed data |
| title_auth | Classification methods for remotely sensed data |
| title_exact_search | Classification methods for remotely sensed data |
| title_full | Classification methods for remotely sensed data Taskin Kavzoglu, Brandt Tso, and Paul M. Mather |
| title_fullStr | Classification methods for remotely sensed data Taskin Kavzoglu, Brandt Tso, and Paul M. Mather |
| title_full_unstemmed | Classification methods for remotely sensed data Taskin Kavzoglu, Brandt Tso, and Paul M. Mather |
| title_short | Classification methods for remotely sensed data |
| title_sort | classification methods for remotely sensed data |
| topic | Soft Computing (DE-588)4455833-8 gnd Methode (DE-588)4038971-6 gnd Satellitenbildauswertung (DE-588)4116326-6 gnd Mustererkennung (DE-588)4040936-3 gnd Fuzzy-Logik (DE-588)4341284-1 gnd Fernerkundung (DE-588)4016796-3 gnd Klassifikation (DE-588)4030958-7 gnd |
| topic_facet | Soft Computing Methode Satellitenbildauswertung Mustererkennung Fuzzy-Logik Fernerkundung Klassifikation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035274704&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kavzoglutaskin classificationmethodsforremotelysenseddata AT tsobrandt classificationmethodsforremotelysenseddata AT matherpaulm classificationmethodsforremotelysenseddata |