{"product_id":"multitemporal-earth-observation-image-analysis-remote-sensing-image-sequences-hardback-9781789451764","title":"Multitemporal Earth Observation Image Analysis; Remote Sensing Image Sequences (Hardback) 9781789451764","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMultitemporal Earth Observation Image Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eRemote Sensing Image Sequences\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eClément Mallet (Edited by), Mallet (Author), Nesrine Chehata (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781789451764, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 25 July 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e272 pages\u003cbr\u003e23.5 x 15.6 x 1.8 cm, 0.667 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eEarth 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMultitemporal Earth Observation Image Analysis\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xi\u003cbr\u003e\u003ci\u003eFrancesca BOVOLO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Broader Application of the Time-SIFT Method: Proof-of-Concept of 3-D-Monitoring Study Cases with Various Spatiotemporal Scales 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDenis FEURER, Sean BEMIS, Guillaume COULOUMA, Hatem MABROUK, Sylvain MASSUEL, Romina Vanessa BARBOSA, Yoann THOMAS, Jérôme AMMANN and Fabrice VINATIER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.2. The Time-SIFT method 4\u003c\/p\u003e \u003cp\u003e1.3. Case studies 8\u003c\/p\u003e \u003cp\u003e1.4. Conclusion 34\u003c\/p\u003e \u003cp\u003e1.5. References 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks 41\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMehmet OZGUR TURKOGLU, Stefano D’ARONCO, Konrad SCHINDLER and Jan Dirk WEGNER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 41\u003c\/p\u003e \u003cp\u003e2.2. Literature 44\u003c\/p\u003e \u003cp\u003e2.3. Background: sequence modeling with recurrent neural networks 49\u003c\/p\u003e \u003cp\u003e2.4. Hierarchical multi-stage convolutional recurrent network 54\u003c\/p\u003e \u003cp\u003e2.5. Experiment 57\u003c\/p\u003e \u003cp\u003e2.6. Summary and future outlook 69\u003c\/p\u003e \u003cp\u003e2.7. References 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Exploiting Multitemporal Multispectral High-resolution Satellite Data toward Annual Land Cover and Crop Type Mapping: A Case Study in Greece 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChristina KARAKIZI, Konstantinos KARANTZALOS and Zacharias KANDYLAKIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 81\u003c\/p\u003e \u003cp\u003e3.2. From raw data to analysis ready datasets 83\u003c\/p\u003e \u003cp\u003e3.3. Classification and mapping 96\u003c\/p\u003e \u003cp\u003e3.4. Data handling and computational challenges 110\u003c\/p\u003e \u003cp\u003e3.5. Conclusions 112\u003c\/p\u003e \u003cp\u003e3.6. Acknowledgments 114\u003c\/p\u003e \u003cp\u003e3.7. References 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Irrigation Monitoring Using High Spatial and Temporal Resolutions Remote Sensing Time Series 123\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHassan BAZZI and Nicolas BAGHDADI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 123\u003c\/p\u003e \u003cp\u003e4.2. Fundamentals behind remote sensing for irrigation mapping 125\u003c\/p\u003e \u003cp\u003e4.3. New methodologies for irrigation mapping using S1 and S2 time series 131\u003c\/p\u003e \u003cp\u003e4.4. Limits and perspectives 142\u003c\/p\u003e \u003cp\u003e4.5. Conclusions 145\u003c\/p\u003e \u003cp\u003e4.6. References 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSantiago BELDA, Luca PIPIA and Jochem VERRELST\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 152\u003c\/p\u003e \u003cp\u003e5.2. Time series processing for gap filling 154\u003c\/p\u003e \u003cp\u003e5.3. Time series processing for phenology indicators estimation 161\u003c\/p\u003e \u003cp\u003e5.4. Fusion of time series products for improved gap filling 163\u003c\/p\u003e \u003cp\u003e5.5. Time series processing toolbox: DATimeS 170\u003c\/p\u003e \u003cp\u003e5.6. Discussion 173\u003c\/p\u003e \u003cp\u003e5.7. Conclusions 176\u003c\/p\u003e \u003cp\u003e5.8. Acknowledgments 177\u003c\/p\u003e \u003cp\u003e5.9. References 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Data-Driven Spatio-Temporal Interpolation for Satellite-Derived Geophysical Tracers 185\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMaxime BEAUCHAMP and Ronan FABLET\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Notations 185\u003c\/p\u003e \u003cp\u003e6.2. Introduction 186\u003c\/p\u003e \u003cp\u003e6.3. Data assimilation 188\u003c\/p\u003e \u003cp\u003e6.4. Data-driven methods 196\u003c\/p\u003e \u003cp\u003e6.5. Application to satellite-derived ocean surface topography datasets 210\u003c\/p\u003e \u003cp\u003e6.6. Conclusion and discussion 216\u003c\/p\u003e \u003cp\u003e6.7. References 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote Sensing 223\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSophie GIFFARD-ROISIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Background 224\u003c\/p\u003e \u003cp\u003e7.2. Handling spatiotemporal data for TC forecasting 229\u003c\/p\u003e \u003cp\u003e7.3. Application 1: intensity forecasting from spatiotemporal reanalysis, a hackathon experiment 231\u003c\/p\u003e \u003cp\u003e7.4. Application 2: trajectory forecasting using fused deep learning 236\u003c\/p\u003e \u003cp\u003e7.5. Applications using recurrent neural networks–convolutional neural networks 242\u003c\/p\u003e \u003cp\u003e7.6. Applications using remote sensing data 246\u003c\/p\u003e \u003cp\u003e7.7. Conclusion, current limitations and open problems 247\u003c\/p\u003e \u003cp\u003e7.8. References 248\u003c\/p\u003e \u003cp\u003eList of Authors 253\u003c\/p\u003e \u003cp\u003eIndex 257\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Earth sciences [\u003ca title=\"See our other books on Earth sciences\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Earth%20sciences%20%5BRB%5D%22\"\u003eRB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446824530200,"sku":"9781789451764","price":103.38,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781789451764.jpg?v=1785114843","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/multitemporal-earth-observation-image-analysis-remote-sensing-image-sequences-hardback-9781789451764","provider":"Freshly Printed Books","version":"1.0","type":"link"}