Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches (Synthesis Lectures on Image, Video, and Multimedia Processing)

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.

Arian Azarang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Texas at Dallas. His research interests include signal and image processing, machine/deep learning, remote sensing, and computer vision. He has authored or co-authored 15 publications in these areas.Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor with the Department of Electrical 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 RealTime Image Processing.