AnyBand-Diff is a spectral-prior-guided diffusion model that unifies remote sensing image generation and band repair while maintaining radiometric fidelity through physics-guided sampling and multi-scale losses.
Diffucd: Unsuper- vised hyperspectral image change detection with semantic correlation diffusion model
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COP-GEN models multimodal Copernicus Earth observation data as conditional distributions via a latent diffusion transformer, producing diverse physically consistent outputs and covering 90% of the real observation manifold on a new stochastic benchmark.
The BDGF framework balances diffusion features to guide multi-branch networks with mutual learning and achieves superior land-cover classification performance on four multimodal remote sensing datasets.
citing papers explorer
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AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors
AnyBand-Diff is a spectral-prior-guided diffusion model that unifies remote sensing image generation and band repair while maintaining radiometric fidelity through physics-guided sampling and multi-scale losses.
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COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data
COP-GEN models multimodal Copernicus Earth observation data as conditional distributions via a latent diffusion transformer, producing diverse physically consistent outputs and covering 90% of the real observation manifold on a new stochastic benchmark.
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Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification
The BDGF framework balances diffusion features to guide multi-branch networks with mutual learning and achieves superior land-cover classification performance on four multimodal remote sensing datasets.