解决云遮挡下遥感土地利用分类难题，融合雷达与光学数据实现高精度实时映射。
Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
- 直接输入带云的光学影像与邻近雷达数据，端到端预测土地利用图。
- 在40,223对样本上达到86.60%整体准确率，优于现有方法。
- 适合需要应对多云区域的实时土地监测应用，如灾害评估与环境追踪。
光学遥感影像常受云和云影污染影响，限制其在近实时土地利用与土地覆盖（LULC）制图中的可靠性。尽管合成孔径雷达（SAR）可穿透云层提供结构信息，但现有融合方法通常假设光学数据可靠，未能充分处理云污染带来的语义不确定性。为此，我们提出CloudLULC-Net，一种端到端异构SAR-光学融合框架，直接从带云污染的哨兵-2影像与时间邻近的哨兵-1 SAR观测中预测LULC图。该网络引入光学可靠性调制以抑制不可靠响应，采用异构信息自适应聚合建模光学与SAR表征间的高阶空间-通道交互，并通过统一语义映射变换器将融合特征组织在面向LULC的潜在空间中。进一步提出语义锚引导优化策略，提升中间语义表示的一致性。为支持该任务，我们构建了CloudLULC-Set，一个大规模基准数据集，包含40,223个经筛选的SAR-光学-标签三元组，覆盖多样地理区域与云况，具备像素级LULC标注。实验表明，CloudLULC-Net在整体准确率（OA）达86.60%，F1分数83.29%，平均交并比（mIoU）73.51%，优于代表性重建优先与端到端融合方法。与现有全球LULC产品对比及不同云覆盖率下的分析进一步验证了其在多云区目标日期制图中的鲁棒性与实用价值。
原文摘要 · Abstract (English)
Optical remote sensing imagery is frequently degraded by cloud and cloud-shadow contamination, which limits its reliability for near-real-time land use and land cover (LULC) mapping. Although synthetic aperture radar (SAR) can provide cloud-penetrating structural information, existing SAR-optical fusion methods often assume reliable optical observations and insufficiently address the semantic uncertainty introduced by cloud contamination. To address this issue, we propose CloudLULC-Net, an end-to-end heterogeneous SAR-optical fusion framework that directly predicts LULC maps from cloud-contaminated Sentinel-2 imagery and temporally adjacent Sentinel-1 SAR observations. The proposed network incorporates optical reliability modulation to suppress unreliable optical responses, heterogeneous information adaptive aggregation to model high-order spatial-channel interactions between optical and SAR representations, and a unified semantic mapping transformer to organize fused features in a LULC-oriented latent space. A semantic anchor-guided optimization strategy is further introduced to improve the consistency of intermediate semantic representations. To support this task, we construct CloudLULC-Set, a large-scale benchmark dataset containing 40,223 curated SAR-optical-label triplets with pixel-level LULC annotations across diverse geographic regions and cloud conditions. Experimental results show that CloudLULC-Net achieves an OA of 86.60%, an F1-score of 83.29%, and an mIoU of 73.51%, outperforming representative heterogeneous reconstruction-first and end-to-end SAR-optical mapping methods. Comparisons with existing global LULC products and analyses under different cloud-cover levels further demonstrate the robustness and practical value of CloudLULC-Net for target-date LULC mapping in cloud-prone regions.The project is publicly available at: https://github.com/RSIIPAC/CloudLULC
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