Document Type

Article

Publication Date

2022

DOI

10.3390/rs14236160

Publication Title

Remote Sensing

Volume

14

Issue

23

Pages

6160 (1-21)

Abstract

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t1 together with low-resolution images at a future time t2 from the same location as inputs and fuses them to generate high-resolution images for the same location at t2. We propose an arithmetic operation applied to the low-resolution images at the two time points in feature space to take care of temporal changes. We evaluated the proposed model on three modality pairs for multimodal temporal image fusion, including downsampled WorldView-2/original WorldView-2, Landsat-8/Sentinel-2, and Sentinel-2/NAIP. Experimental results show that our model outperforms traditional algorithms and recent deep learning-based models by large margins in most scenarios, achieving sharp fused images while appropriately addressing temporal changes.

Rights

© 2022 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Data Availability

Article States: Our source code and data can be found from the following link: https://github.com/reshadshuvo123/Image-fusion.

Original Publication Citation

Hoque, M. R. U., Wu, J., Kwan, C., Koperski, K., & Li, J. (2022). ArithFusion: An arithmetic deep model for temporal remote sensing image fusion. Remote Sensing, 14(23), 1-21, Article 6160. https://doi.org/10.3390/rs14236160

ORCID

0000-0003-4055-2582 (Hoque), 0000-0003-0173-4463 (Wu), 0000-0003-0091-6986 (Li)

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