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Image Fusion in Remote Sensing [electronic resource] : Conventional and Deep Learning Approaches / by Arian Azarang, Nasser Kehtarnavaz.

By: Azarang, Arian [author.].
Contributor(s): Kehtarnavaz, Nasser [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Image, Video, and Multimedia Processing: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XI, 81 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031022562.Subject(s): Engineering | Electrical engineering | Signal processing | Technology and Engineering | Electrical and Electronic Engineering | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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