Beyond the Clouds: Reliable and Cloud-Aware Spatiotemporal Fusion via Adversarial Regression Wavelets
Sichen Lu, Mingfei Li, Juanjuan Jing, Junhua Yu, Lei Yang, Boyang Nie, Jinsong Zhou
Abstract
Spatiotemporal fusion (STF) bridges the gap between temporal and spatial resolutions in satellite imagery, enabling effective monitoring of Earth's surface dynamics. However, existing methods rely on cloud-free reference images, a constraint that fails in realistic, cloud-prone scenarios. To overcome this, we propose the Cloud-Aware Wavelet Generative Adversarial Network (CLAW-GAN), a novel framework for high-fidelity reconstruction under cloud-contaminated conditions. CLAW-GAN introduces Regression Wavelet Analysis (RWA) to decouple spectral backgrounds from structural details. While a change-aware gated mechanism accounts for land-cover changes, the Frequency-Separated Fusion (FSF) module then independently integrates these components. To ensure visual realism, a multi-scale discriminator operates in the wavelet domain, enforcing consistency across high-frequency subbands to minimize artifacts. Evaluated on the newly introduced Global Cloud-shrouded Agricultural Regions (GCAR) benchmark and the simulated Daxing dataset, CLAW-GAN achieves state-of-the-art performance and demonstrates superior robustness across varying cloud coverage.
BibTeX
@inproceedings{ijcai2026_beyondthecloudsr,
title = {Beyond the Clouds: Reliable and Cloud-Aware Spatiotemporal Fusion via Adversarial Regression Wavelets},
author = {Sichen Lu and Mingfei Li and Juanjuan Jing and Junhua Yu and Lei Yang and Boyang Nie and Jinsong Zhou},
booktitle = {IJCAI 2026},
year = {2026}
}