Physics-Guided VLM Priors for All-Cloud Removal

TL;DR

This paper introduces PhyVLM-CR, a novel cloud removal method that integrates vision-language models with physical scattering models, enabling unified, high-fidelity cloud removal across heterogeneous cloud types without explicit cloud boundary decisions.

Contribution

It proposes a new approach that transforms VLM-derived semantic priors into physical parameters and uses a confidence map for adaptive, unified cloud removal, improving over existing methods.

Findings

Achieves high-fidelity cloud removal with minimal hallucination.

Outperforms existing methods in quantitative accuracy.

Provides coherent removal across mixed cloud scenes.

Abstract

Cloud removal is a fundamental challenge in optical remote sensing due to the heterogeneous degradation. Thin clouds distort radiometry via partial transmission, while thick clouds occlude the surface. Existing pipelines separate thin-cloud correction from thick-cloud reconstruction, requiring explicit cloud-type decisions and often leading to error accumulation and discontinuities in mixed-cloud scenes. Therefore, a novel approach named Physical-VLM All-Cloud Removal (PhyVLM-CR) that integrates the semantic capability of Vision-Language Model (VLM) into a physical restoration model, achieving high-fidelity unified cloud removal. Specifically, the cognitive prior from a VLM (e.g., Qwen) is transformed into physical scattering parameters and a hallucination confidence map. Leveraging this confidence map as a continuous soft gate, our method achieves a unified restoration via adaptive weighting: it prioritizes physical inversion in high-transmission regions to preserve radiometric fidelity, while seamlessly transitioning to temporal reference reconstruction in low-confidence occluded areas. This mechanism eliminates the need for explicit boundary delineation, ensuring a coherent removal across heterogeneous cloud covers. Experiments on real-world Sentinel-2 surface reflectance imagery confirm that our approach achieves a remarkable balance between cloud removal and content preservation, delivering hallucination-free results with substantially improved quantitative accuracy compared to existing methods.