Deep learning multi sensor earth (3 Ergebnisse)
- Softcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 137,95
EUR 7,60 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 3 verfügbar
Zustand: New.
- Softcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 137,16
EUR 14,61 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Paperback. Zustand: Brand New. 350 pages. 9.00x6.00x8.93 inches. In Stock.
- Weitere Bilder
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 208,00
EUR 63,63 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
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 da…ta 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.
