LITMIR.BIZ

      1  Cover

      2  Title Page

      3  Copyright

      4  Preface

      5  List of Notations

      6  1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1.1. Introduction 1.2. Methodology 1.3. Examples of experimental results 1.4. Conclusion 1.5. Acknowledgments 1.6. References

      7  2 Pixel-based Classification Techniques for Satellite Image Time Series 2.1. Introduction 2.2. Basic concepts in supervised remote sensing classification 2.3. Traditional classification algorithms 2.4. Classification strategies based on temporal feature representations 2.5. Deep learningapproaches 2.6. References

      8  3 Semantic Analysis of Satellite Image Time Series 3.1. Introduction 3.2. Why are semantics neededin SITS? 3.3. Similaritymetrics 3.4. Feature methods 3.5. Classification methods 3.6. Conclusion 3.7. Acknowledgments 3.8. References

      9  4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 4.1. Introduction 4.2. Annual time series 4.3. Dense time series analysis using all available data 4.4. Deep learning-based time series analysis approaches 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 4.6. References

      10  5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 5.1. Introduction 5.2. Satellite-based earthquake damage assessment 5.3. Pre-processing of satellite images before damage assessment 5.4. Multi-source image analysis 5.5. Contextual feature mining for damage assessment 5.6. Multi-temporal image analysis for damage assessment 5.7. Understanding damage following an earthquake using satellite-based SAR 5.8. Use of auxiliary data sources 5.9. Damage grades 5.10. Conclusionand discussion 5.11. References

      11  6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 6.1. Introduction 6.2. Coarse- to fine-grained change of state dataset 6.3. Deep transfer learning models for change of state classification 6.4. Change of state analysis 6.5. Conclusion 6.6. Acknowledgments 6.7. References

      12  List of Authors

      13  Index

      14  Summary of Volume 1

      15  End User License Agreement

      1  Cover

      2  Table of Contents

      3  Title page

      4  Copyright

      5  Preface

      6 

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