Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139815991 ISBN 13: 9786139815999
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Applications of Deep Learning to Radar Polarimetry | A Physics First Approach to Machine Learning in Radar Earth Observation Applications | Shaunak de | Taschenbuch | 212 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139815999 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139815991 ISBN 13: 9786139815999
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Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2023
ISBN 10: 1839536179 ISBN 13: 9781839536175
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Sprache: Englisch
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In den WarenkorbZustand: New. Introduces deep learning for classification, covering recent improvements in image segmentation and encoding priors, anomaly detection and target recognition, and domain adaptabilityIncludes both learning representations and unsupervised de.
Sprache: Englisch
Verlag: Institution Of Engineering & Technology Jul 2023, 2023
ISBN 10: 1839536179 ISBN 13: 9781839536175
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - Using machine and deep learning techniques the authors introduce pre-processing methods applied to satellite images to identify land cover features, detect object, classify crops, recognize targets, and monitor and support earth resources. Readers will need a basic understanding of computing, remote sensing and image interpretation.
Sprache: Englisch
Verlag: Elsevier Science Feb 2025, 2025
ISBN 10: 0443264848 ISBN 13: 9780443264849
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.
Sprache: Englisch
Verlag: Elsevier - Health Sciences Division Jun 2025, 2025
ISBN 10: 0443247129 ISBN 13: 9780443247125
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring.This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.