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Idioma: Inglés
Publicado por Springer International Publishing, 2021
ISBN 10: 3031011287 ISBN 13: 9783031011283
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and spectral (multispectral) images that are captured by different sensors on satellites. This book addresses image fusion approaches for remote sensing applications. Both conventional and deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches include component substitution, multi-resolution, and model-based algorithms. Then, the recently developed deep learning approaches involving single-objective and multi-objective loss functions are discussed. Experimental results are provided comparing conventional and deep learning approaches in terms of both low-resolution and full-resolution objective metrics that are commonly used in remote sensing. The book is concluded by stating anticipated future trends in pansharpening or image fusion in remote sensing.
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Añadir al carritoTaschenbuch. Condición: Neu. Image Fusion in Remote Sensing | Conventional and Deep Learning Approaches | Arian Azarang (u. a.) | Taschenbuch | Synthesis Lectures on Image, Video, and Multimedia Processing | xi | Englisch | 2021 | Springer | EAN 9783031011283 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Idioma: Inglés
Publicado por Springer International Publishing Feb 2021, 2021
ISBN 10: 3031011287 ISBN 13: 9783031011283
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and spectral (multispectral) images that are captured by different sensors on satellites. This book addresses image fusion approaches for remote sensing applications. Both conventional and deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches include component substitution, multi-resolution, and model-based algorithms. Then, the recently developed deep learning approaches involving single-objective and multi-objective loss functions are discussed. Experimental results are provided comparing conventional and deep learning approaches in terms of both low-resolution and full-resolution objective metrics that are commonly used in remote sensing. The book is concluded by stating anticipated future trends in pansharpening or image fusion in remote sensing. 96 pp. Englisch.
Idioma: Inglés
Publicado por Springer International Publishing, 2021
ISBN 10: 3031011287 ISBN 13: 9783031011283
Librería: moluna, Greven, Alemania
EUR 28,42
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Arian Azarang received the BS degree and the first rank award from Shiraz University, Iran, in 2015, and the MS degree in electrical engineering from Tarbiat Modares University, Iran, in 2017. He received his Ph.D. degree in electrical engineering fr.
Idioma: Inglés
Publicado por Springer, Birkhäuser Feb 2021, 2021
ISBN 10: 3031011287 ISBN 13: 9783031011283
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and spectral (multispectral) images that are captured by different sensors on satellites. This book addresses image fusion approaches for remote sensing applications. Both conventional and deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches include component substitution, multi-resolution, and model-based algorithms. Then, the recently developed deep learning approaches involving single-objective and multi-objective loss functions are discussed. Experimental results are provided comparing conventional and deep learning approaches in terms of both low-resolution and full-resolution objective metrics that are commonly used in remote sensing. The book is concluded by stating anticipated future trends in pansharpening or image fusion in remote sensing.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 96 pp. Englisch.