
Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches
Morgan & Claypool Publishers
Published on 28. February 2021
Book
Hardback
93 pages
978-1-63639-076-5 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
San Rafael
United States
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Weight
333 gr
ISBN-13
978-1-63639-076-5 (9781636390765)
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Other editions
Additional editions

Arian Azarang | Nasser Kehtarnavaz
Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches
Book
02/2021
Morgan & Claypool Publishers
€66.99
Shipment within 15-20 days
Content
- Preface
- Introduction
- Introduction to Remote Sensing
- Conventional Image Fusion Approaches in Remote Sensing
- Deep Learning-Based Image Fusion Approaches in Remote Sensing
- Unsupervised Generative Model for Pansharpening
- Experimental Studies
- Anticipated Future Trend
- Authors' Biographies
- Index
- Introduction
- Introduction to Remote Sensing
- Conventional Image Fusion Approaches in Remote Sensing
- Deep Learning-Based Image Fusion Approaches in Remote Sensing
- Unsupervised Generative Model for Pansharpening
- Experimental Studies
- Anticipated Future Trend
- Authors' Biographies
- Index