Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches

Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches

Azarang, Arian
Kehtarnavaz, Nasser

Paperback

Series: Synthesis Lectures on Image, Video, and Multimedia Processin

Technology & Engineering

ISBN10: 3031011287
ISBN13: 9783031011283
Publisher: Springer Nature
Published: Feb 18 2021
Pages: 81
Weight: 0.40
Height: 0.20 Width: 7.50 Depth: 9.25
Language: English
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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