让遥感图像去云同时做分割，效果更好且更省资源。
Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation
- 用可学习的退化提示融合雷达与光学数据，仅在云遮挡处引入雷达信息。
- 在基准数据集上比顶尖方法高0.18 dB PSNR，参数量仅为15%。
- 适合需要高质量分析级遥感数据的研究者和应用开发者。
光学遥感影像对地球观测至关重要，但持续的云层遮挡限制了其下游应用。现有去云（CR）方法多优化低层级保真度，易过度平滑纹理和边界，导致视觉还原与语义可用性不匹配。为此，我们提出任务驱动的多模态框架TDP-CR，联合实现去云与地表覆盖分割。核心是提示引导融合（PGF）机制，利用可学习的退化提示编码云层厚度与空间不确定性。通过结合全局通道上下文与局部提示条件空间偏置，PGF仅在光学数据受损区域自适应融合合成孔径雷达（SAR）信息。此外，设计参数高效的两阶段训练策略，解耦重建与语义表征学习。在LuojiaSET-OSFCR数据集上的实验表明，TDP-CR以仅15%参数量超越主流基线0.18 dB PSNR，且在mIoU上较多任务竞争者提升1.4%，有效产出分析就绪数据。
原文摘要 · Abstract (English)
Optical remote sensing imagery is indispensable for Earth observation, yet persistent cloud occlusion limits its downstream utility. Most cloud removal (CR) methods are optimized for low-level fidelity and can over-smooth textures and boundaries that are critical for analysis-ready data (ARD), leading to a mismatch between visually plausible restoration and semantic utility. To bridge this gap, we propose TDP-CR, a task-driven multimodal framework that jointly performs cloud removal and land-cover segmentation. Central to our approach is a Prompt-Guided Fusion (PGF) mechanism, which utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty. By combining global channel context with local prompt-conditioned spatial bias, PGF adaptively integrates Synthetic Aperture Radar (SAR) information only where optical data is corrupted. We further introduce a parameter-efficient two-phase training strategy that decouples reconstruction and semantic representation learning. Experiments on the LuojiaSET-OSFCR dataset demonstrate the superiority of our framework: TDP-CR surpasses heavy state-of-the-art baselines by 0.18 dB in PSNR while using only 15\% of the parameters, and achieves a 1.4\% improvement in mIoU consistently against multi-task competitors, effectively delivering analysis-ready data.
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