Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 130,78
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: New.
Librería: Best Price, Torrance, CA, Estados Unidos de America
EUR 125,23
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: New. SUPER FAST SHIPPING.
Publicado por Elsevier - Health Sciences Division, Philadelphia, 2025
ISBN 10: 0443264848 ISBN 13: 9780443264849
Idioma: Inglés
Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
EUR 140,02
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 133,94
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 142,49
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 156,00
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
EUR 135,74
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 350 pages. 9.00x6.00x8.93 inches. In Stock.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 151,67
Convertir monedaCantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Publicado por Elsevier - Health Sciences Division, 2025
ISBN 10: 0443264848 ISBN 13: 9780443264849
Idioma: Inglés
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 148,83
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 1000.
Publicado por Elsevier - Health Sciences Division, Philadelphia, 2025
ISBN 10: 0443264848 ISBN 13: 9780443264849
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 142,39
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Original o primera edición
EUR 174,82
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: New. 2025. 1st Edition. paperback. . . . . .
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 217,52
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: New. 2025. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
EUR 199,23
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 350 pages. 9.00x6.00x8.93 inches. In Stock.
Publicado por Elsevier Science Feb 2025, 2025
ISBN 10: 0443264848 ISBN 13: 9780443264849
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 210,50
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: 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.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 125,73
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: new. Questo è un articolo print on demand.