Pansharpening for Thin-Cloud Contaminated Remote Sensing Images: A Unified Framework and Benchmark Dataset

2026-03-16

AI Summary

This work addresses the challenge of spatial blurring and spectral distortion in remote sensing imagery caused by thin cloud contamination, a problem exacerbated by conventional pipeline approaches that separately handle cloud removal and pansharpening, often leading to cumulative errors. To overcome this, the authors propose Pan-TCR, an end-to-end unified framework, and introduce PanTCR-GF2—the first real-world thin-cloud pansharpening dataset. Central to their approach is a frequency-domain decoupled restoration mechanism: the near-infrared band guides cloud-robust amplitude recovery, while the panchromatic image enhances high-frequency phase structures. An interactive cross-frequency consistency module further enables multimodal co-optimization. Extensive experiments demonstrate that Pan-TCR significantly outperforms existing methods on both synthetic and real-world data, establishing a new benchmark for pansharpening under thin cloud conditions and confirming its robustness and generalization capability.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Pansharpening under thin cloudy conditions is a practically significant yet rarely addressed task, challenged by simultaneous spatial resolution degradation and cloud-induced spectral distortions. Existing methods often address cloud removal and pansharpening sequentially, leading to cumulative errors and suboptimal performance due to the lack of joint degradation modeling. To address these challenges, we propose a Unified Pansharpening Model with Thin Cloud Removal (Pan-TCR), an end-to-end framework that integrates physical priors. Motivated by theoretical analysis in the frequency domain, we design a frequency-decoupled restoration (FDR) block that disentangles the restoration of multispectral image (MSI) features into amplitude and phase components, each guided by complementary degradation-robust prompts: the near-infrared (NIR) band amplitude for cloud-resilient restoration, and the panchromatic (PAN) phase for high-resolution structural enhancement. To ensure coherence between the two components, we further introduce an interactive inter-frequency consistency (IFC) module, enabling cross-modal refinement that enforces consistency and robustness across frequency cues. Furthermore, we introduce the first real-world thin-cloud contaminated pansharpening dataset (PanTCR-GF2), comprising paired clean and cloudy PAN-MSI images, to enable robust benchmarking under realistic conditions. Extensive experiments on real-world and synthetic datasets demonstrate the superiority and robustness of Pan-TCR, establishing a new benchmark for pansharpening under realistic atmospheric degradations.

Problem

• pansharpening
• thin-cloud contamination
• spectral distortion
• spatial resolution degradation
• remote sensing

Innovation

• pansharpening
• thin-cloud removal
• frequency-decoupled restoration
• interactive inter-frequency consistency
• benchmark dataset