2026· IEEE Geoscience and Remote Sensing Letters· Vol 23, pp. 5002605-5002605· 0 citations· 21 references
Abstract
Cloud removal models require paired multispectral observations, yet such data remain scarce, and existing synthetic methods often fail to preserve both realistic cloud geometry and band-wise spectral consistency. We present a traceable six-band Sentinel-2 benchmark dataset alongside a diffusion-mask-driven synthetic augmentation pipeline. The dataset contains approximately 73 000 paired patches from six globally distributed Sentinel-2 tiles, with a stratified in-domain test subset that supports evaluation across cloud-coverage levels. Spectral consistency filtering on clear pixels helps ensure reliable restoration targets. To mitigate data scarcity, we decouple geometric modeling (using a denoising diffusion probabilistic model (DDPM) to capture cloud structures) from radiometric rendering (via a band-sensitive scattering model). Evaluations show two complementary effects: additive synthetic augmentation improves in-domain real-data efficiency, while fixed-budget RGB mixed real/synthetic training improves cross-domain RICE performance. Curated real pairs remain important for spectral anchoring. The dataset and pipeline offer a reproducible benchmark for multispectral cloud removal under data-limited regimes.
Cloud contamination severely limits the availability of optical remote sensing data for water quality estimation, which demands strict spectral fidelity rather than mere visual restoration. To address this challenge, we propose the water-aware diffusion transformer (WaDiT), a novel conditional diffusion framework that...
Yi-Ming Yang, Fang-Ling Pu, Yue Dai et al.· IEEE Geoscience and Remote S...· 0 citations
The Cloud-Aware Wavelet Generative Adversarial Network (CLAW-GAN), a novel framework for high-fidelity reconstruction under cloud-contaminated conditions, achieves state-of-the-art performance and demonstrates superior robustness across varying cloud coverage.
Si-Chen Lu, Ming-Fei Li, Juan-Juan Jing et al.· Proceedings of the Thirty-Fi...· 0 citations
Reconstructing multi-temporal remote sensing images contaminated by thick clouds remains challenging due to the over smoothing and cloud mask sensitivity of single-scale tensor methods. To address this, we propose the Multi-Scale Bilateral Tensor Ring decomposition with Gradient Fidelity (MS-BTR-GF) model. By construct...
Xin-Yuan Geng, Yi-Yan Chen, Shu-Ting Wang et al.· Conference on Advanced Algor...· 0 citations
This study investigates whether geospatial priors derived from AlphaEarth Foundation model embeddings can simultaneously enhance SR reconstruction and facilitate cloud-affected region recovery and shows that AEF conditioning provides an implicit cloud-inpainting effect within the MISR framework, enabling simultaneous s...
E. Burnaev, S. Illarionova, Usman Tasuev et al.· IEEE Access· 0 citations
Cloud removal is crucial for preprocessing optical remote sensing images affected by natural cloud contamination, which is highly diverse and variable. Clouds of varying thickness often occur simultaneously, where the boundary between these layers is ambiguous and hard to quantitatively define. Consequently, advanced d...
Yu-Jun Guo, Wei He, He Huang et al.· IEEE Transactions on Geoscie...· 0 citations
Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and observation settings. We introduce GeoCR, a generalist model that unifies RGB-only-based CR and multispectral-based CR from single- or multi-temporal cloudy observations, w...
Jeonghyeok Do, Munchurl Kim· 0 citations
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