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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.
1. Broader Application of the Time-SIFT Method: Proof-of-Concept of 3-D-Monitoring Study Cases with Various Spatiotemporal Scales, Denis Feurer, Sean Bemis, Guillaume Coulouma, Hatem Mabrouk, Sylvain Massuel, Romina Vanessa Barbosa, Yoann Thomas, Jérôme Ammann and Fabrice Vinatier.
2. Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks, Mehmet Ozgur Turkoglu, Stefano D’Aronco, Konrad Schindler and Jan Dirk Wegner.
3. Exploiting Multitemporal Multispectral High-resolution Satellite Data toward Annual Land Cover and Crop Type Mapping: A Case Study in Greece, Christina Karakizi, Konstantinos Karantzalos and Zacharias Kandylakis.
4. Irrigation Monitoring Using High Spatial and Temporal Resolutions Remote Sensing Time Series, Hassan Bazzi and Nicolas Baghdadi.
5. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring, Santiago Belda, Luca Pipia and Jochem Verrelst.
6. Data-Driven Spatio-Temporal Interpolation for Satellite-Derived Geophysical Tracers, Maxime Beauchamp and Ronan Fablet.
7. Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote Sensing, Sophie Giffard-Roisin.
Clément Mallet
Clément Mallet est chercheur à l’Institut national de l’information géographique et forestière (IGN) où il s’occupe en particulier des recherches en télédétection au sein du Laboratoire des sciences et technologies de l’information géographique.
