Overview of remote sensing image fusion based on deep learning

Qian Wang

doi:10.1117/12.2692871

7 March 2024 Overview of remote sensing image fusion based on deep learning

Qian Wang

Proceedings Volume 13084, MIPPR 2023: Multispectral Image Acquisition, Processing, and Analysis; 130840O (2024) https://doi.org/10.1117/12.2692871
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

With the widespread application and rapid development of remote sensing technology, the quality requirements for remote sensing images are gradually improving. Currently, relying solely on one sensor is difficult to ensure high signal-to- noise ratio while capturing images with high spatial and spectral resolution. Remote sensing image fusion technology obtains high-quality images by combining the spatial information and spectral (or spectral) information of different sensors. Meanwhile, in recent years, deep learning theory has developed rapidly and is widely applied in image processing such as remote sensing image fusion. Therefore, in order to gain a more systematic understanding of the current status of remote sensing image fusion based on deep learning and promote the development of remote sensing image fusion, this article first introduces commonly used remote sensing satellite images and traditional image fusion algorithms; Then, the remote sensing image fusion algorithm based on deep learning is emphasized, and the advantages and disadvantages of deep learning fusion methods (PanNet, LPPN, WSDFNet) are compared and analyzed; Finally, the future prospects of remote sensing image fusion methods based on deep learning are presented.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Wang "Overview of remote sensing image fusion based on deep learning", Proc. SPIE 13084, MIPPR 2023: Multispectral Image Acquisition, Processing, and Analysis, 130840O (7 March 2024); https://doi.org/10.1117/12.2692871

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available