Image Fusion in Remote Sensing : Conventional and Deep Learning Approaches
Azarang, Arian
Image Fusion in Remote Sensing : Conventional and Deep Learning Approaches by Arian Azarang, Nasser Kehtarnavaz - 1st edition 2021 - 1 recurso en línea (XI, 81 páginas) - Synthesis Lectures on Image Video and Multimedia Processing 1559-8144 .
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.
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.
9783031022562
10.1007/978-3-031-02256-2 doi
Teledetección
Proceso de imágenes
G70.4 / 2021 EB
Image Fusion in Remote Sensing : Conventional and Deep Learning Approaches by Arian Azarang, Nasser Kehtarnavaz - 1st edition 2021 - 1 recurso en línea (XI, 81 páginas) - Synthesis Lectures on Image Video and Multimedia Processing 1559-8144 .
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.
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.
9783031022562
10.1007/978-3-031-02256-2 doi
Teledetección
Proceso de imágenes
G70.4 / 2021 EB