Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches
Published on 18. February 2021
Book
Paperback/Softback
XI, 81 pages
978-3-031-01128-3 (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
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
XI, 81 p.
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 6 mm
Weight
197 gr
ISBN-13
978-3-031-01128-3 (9783031011283)
DOI
10.1007/978-3-031-02256-2
Schweitzer Classification
Other editions
Additional editions
Arian Azarang | Nasser Kehtarnavaz
Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches
E-Book
05/2022
1st Edition
Springer
€28.88
Available for download
Persons
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 from the University of Texas at Dallas in 2021. He was recognized as the Honorable Mention of the David Daniel Thesis Award for the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas. He is currently working as a Postdoctoral Research Associate at the University of North Carolina at Chapel Hill. His research interests include signal and image processing, applied deep learning, and speech recognition and enhancement. He has thus far authored or co-authored 21 scholarly publications in these areas. He has recently become an Associate Editor of the Springer journal Signal, Image and Video Processing.
Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor with the Department ofElectrical and Computer Engineering and the Director of the Embedded Machine Learning Laboratory at the University of Texas at Dallas. His research interests include signal and image processing, machine/deep learning, and real-time implementation on embedded processors. He has authored or co-authored 10 books and more than 400 journal papers, conference papers, patents, manuals, and editorials in these areas. He is a Fellow of IEEE, a Fellow of SPIE, and a Licensed Professional Engineer. He is currently serving as Editor-in-Chief of Journal of Real Time Image Processing .
Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor with the Department ofElectrical and Computer Engineering and the Director of the Embedded Machine Learning Laboratory at the University of Texas at Dallas. His research interests include signal and image processing, machine/deep learning, and real-time implementation on embedded processors. He has authored or co-authored 10 books and more than 400 journal papers, conference papers, patents, manuals, and editorials in these areas. He is a Fellow of IEEE, a Fellow of SPIE, and a Licensed Professional Engineer. He is currently serving as Editor-in-Chief of Journal of Real Time Image Processing .
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.