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Image Fusion in Remote Sensing

Image Fusion in Remote Sensing

Conventional and Deep Learning Approaches

Arian Azarang, Nasser Kehtarnavaz

Springer
Collection: Synthesis Lectures on Image, Video, and Multimedia Processing
Publication date : 2022-05-31

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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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About the book

Author

Arian Azarang , Nasser Kehtarnavaz

Imprint

Springer

Collection

Synthesis Lectures on Image, Video, and Multimedia Processing

Publication date

2022-05-31

Pages

81 pages

Print ISBN

9783031011283

Language

English

Ebook informations

EAN PDF

9783031022562

Price

£24.99

Copy count 0

Print count 8

File size 45425 Ko

Informations accessibility

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About author(s)

Arian Azarang

Nasser Kehtarnavaz

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