Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival…mehr
Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives. The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Clément Mallet is a senior scientist in remote sensing for land-cover mapping issues at the LaSTIG Laboratory (Gustave Eiffel University, IGN, French Mapping Agency), France. He is also Editor-in-Chief of the ISPRS Journal of Photogrammetry and Remote Sensing. Nesrine Chehata is a senior lecturer in geomatics and AI for Earth observation at ENSEGID-Bordeaux INP, France. She is also President and Co-founder of AGEOS (African Association for Geospatial Development), President of FNAACC (National Forum of Climate Change Adaptation Actors in Tunisia) and an IEEE GRSS senior member.
Inhaltsangabe
Foreword xi Francesca BOVOLO Chapter 1. Broader Application of the Time-SIFT Method: Proof-of-Concept of 3-D-Monitoring Study Cases with Various Spatiotemporal Scales 1 Denis FEURER, Sean BEMIS, Guillaume COULOUMA, Hatem MABROUK, Sylvain MASSUEL, Romina Vanessa BARBOSA, Yoann THOMAS, Jérôme AMMANN and Fabrice VINATIER 1.1. Introduction 1 1.2. The Time-SIFT method 4 1.3. Case studies 8 1.4. Conclusion 34 1.5. References 35 Chapter 2. Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks 41 Mehmet OZGUR TURKOGLU, Stefano D'ARONCO, Konrad SCHINDLER and Jan Dirk WEGNER 2.1. Introduction 41 2.2. Literature 44 2.3. Background: sequence modeling with recurrent neural networks 49 2.4. Hierarchical multi-stage convolutional recurrent network 54 2.5. Experiment 57 2.6. Summary and future outlook 69 2.7. References 72 Chapter 3. Exploiting Multitemporal Multispectral High-resolution Satellite Data toward Annual Land Cover and Crop Type Mapping: A Case Study in Greece 81 Christina KARAKIZI, Konstantinos KARANTZALOS and Zacharias KANDYLAKIS 3.1. Introduction 81 3.2. From raw data to analysis ready datasets 83 3.3. Classification and mapping 96 3.4. Data handling and computational challenges 110 3.5. Conclusions 112 3.6. Acknowledgments 114 3.7. References 114 Chapter 4. Irrigation Monitoring Using High Spatial and Temporal Resolutions Remote Sensing Time Series 123 Hassan BAZZI and Nicolas BAGHDADI 4.1. Introduction 123 4.2. Fundamentals behind remote sensing for irrigation mapping 125 4.3. New methodologies for irrigation mapping using S1 and S2 time series 131 4.4. Limits and perspectives 142 4.5. Conclusions 145 4.6. References 146 Chapter 5. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring 151 Santiago BELDA, Luca PIPIA and Jochem VERRELST 5.1. Introduction 152 5.2. Time series processing for gap filling 154 5.3. Time series processing for phenology indicators estimation 161 5.4. Fusion of time series products for improved gap filling 163 5.5. Time series processing toolbox: DATimeS 170 5.6. Discussion 173 5.7. Conclusions 176 5.8. Acknowledgments 177 5.9. References 177 Chapter 6. Data-Driven Spatio-Temporal Interpolation for Satellite-Derived Geophysical Tracers 185 Maxime BEAUCHAMP and Ronan FABLET 6.1. Notations 185 6.2. Introduction 186 6.3. Data assimilation 188 6.4. Data-driven methods 196 6.5. Application to satellite-derived ocean surface topography datasets 210 6.6. Conclusion and discussion 216 6.7. References 218 Chapter 7. Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote Sensing 223 Sophie GIFFARD-ROISIN 7.1. Background 224 7.2. Handling spatiotemporal data for TC forecasting 229 7.3. Application 1: intensity forecasting from spatiotemporal reanalysis, a hackathon experiment 231 7.4. Application 2: trajectory forecasting using fused deep learning 236 7.5. Applications using recurrent neural networks-convolutional neural networks 242 7.6. Applications using remote sensing data 246 7.7. Conclusion, current limitations and open problems 247 7.8. References 248 List of Authors 253 Index 257
Foreword xi Francesca BOVOLO Chapter 1. Broader Application of the Time-SIFT Method: Proof-of-Concept of 3-D-Monitoring Study Cases with Various Spatiotemporal Scales 1 Denis FEURER, Sean BEMIS, Guillaume COULOUMA, Hatem MABROUK, Sylvain MASSUEL, Romina Vanessa BARBOSA, Yoann THOMAS, Jérôme AMMANN and Fabrice VINATIER 1.1. Introduction 1 1.2. The Time-SIFT method 4 1.3. Case studies 8 1.4. Conclusion 34 1.5. References 35 Chapter 2. Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks 41 Mehmet OZGUR TURKOGLU, Stefano D'ARONCO, Konrad SCHINDLER and Jan Dirk WEGNER 2.1. Introduction 41 2.2. Literature 44 2.3. Background: sequence modeling with recurrent neural networks 49 2.4. Hierarchical multi-stage convolutional recurrent network 54 2.5. Experiment 57 2.6. Summary and future outlook 69 2.7. References 72 Chapter 3. Exploiting Multitemporal Multispectral High-resolution Satellite Data toward Annual Land Cover and Crop Type Mapping: A Case Study in Greece 81 Christina KARAKIZI, Konstantinos KARANTZALOS and Zacharias KANDYLAKIS 3.1. Introduction 81 3.2. From raw data to analysis ready datasets 83 3.3. Classification and mapping 96 3.4. Data handling and computational challenges 110 3.5. Conclusions 112 3.6. Acknowledgments 114 3.7. References 114 Chapter 4. Irrigation Monitoring Using High Spatial and Temporal Resolutions Remote Sensing Time Series 123 Hassan BAZZI and Nicolas BAGHDADI 4.1. Introduction 123 4.2. Fundamentals behind remote sensing for irrigation mapping 125 4.3. New methodologies for irrigation mapping using S1 and S2 time series 131 4.4. Limits and perspectives 142 4.5. Conclusions 145 4.6. References 146 Chapter 5. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring 151 Santiago BELDA, Luca PIPIA and Jochem VERRELST 5.1. Introduction 152 5.2. Time series processing for gap filling 154 5.3. Time series processing for phenology indicators estimation 161 5.4. Fusion of time series products for improved gap filling 163 5.5. Time series processing toolbox: DATimeS 170 5.6. Discussion 173 5.7. Conclusions 176 5.8. Acknowledgments 177 5.9. References 177 Chapter 6. Data-Driven Spatio-Temporal Interpolation for Satellite-Derived Geophysical Tracers 185 Maxime BEAUCHAMP and Ronan FABLET 6.1. Notations 185 6.2. Introduction 186 6.3. Data assimilation 188 6.4. Data-driven methods 196 6.5. Application to satellite-derived ocean surface topography datasets 210 6.6. Conclusion and discussion 216 6.7. References 218 Chapter 7. Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote Sensing 223 Sophie GIFFARD-ROISIN 7.1. Background 224 7.2. Handling spatiotemporal data for TC forecasting 229 7.3. Application 1: intensity forecasting from spatiotemporal reanalysis, a hackathon experiment 231 7.4. Application 2: trajectory forecasting using fused deep learning 236 7.5. Applications using recurrent neural networks-convolutional neural networks 242 7.6. Applications using remote sensing data 246 7.7. Conclusion, current limitations and open problems 247 7.8. References 248 List of Authors 253 Index 257
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