Image Fusion in Remote Sensing Conventional and Deep Learning Approaches |
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| Author:
| Azarang, Arian
Kehtarnavaz, Nasser |
| Series title: | Synthesis Lectures on Image, Video, and Multimedia Processing Ser. |
| ISBN: | 978-3-031-01128-3 |
| Publication Date: | Feb 2021 |
| Publisher: | Springer International Publishing AG
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| Imprint: | Springer |
| Book Format: | Paperback |
| List Price: | USD $34.99USD $29.99 |
Book Description:
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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...
More DescriptionImage 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.