模拟云层干扰提升遥感分类模型鲁棒性评估能力
Not every day is a sunny day: Synthetic cloud injection for deep land cover segmentation robustness evaluation across data sources
- 用真实云层分布生成算法注入合成云，模拟热带地区常见遮挡
- 融合雷达与光学数据使模型在有云场景下性能显著提升
- 轻量级注入归一化指数，几乎不增加计算量却提升分类精度
监督式深度学习用于地表覆盖语义分割依赖标注卫星数据，但现有大多数哨兵-2数据集无云，限制了其在多云热带地区的适用性。为评估此问题的严重程度，我们开发了一种模拟真实云覆盖的云注入算法，用于测试哨兵-1雷达数据能否弥补光学影像被云遮挡造成的数据缺失。同时，针对深度网络编码器下采样中空间与光谱细节丢失的问题，提出一种轻量化方法，在解码层末尾注入归一化差异指数（NDIs），使模型在极小计算开销下保留关键空间特征。在DFC2020数据集上，注入NDIs使U-Net性能提升1.99%，DeepLabV3提升2.78%。在云覆盖条件下，结合哨兵-1数据相比仅使用光学数据显著提升所有模型表现，验证了雷达-光学融合在复杂大气条件下的有效性。
原文摘要 · Abstract (English)
Supervised deep learning for land cover semantic segmentation (LCS) relies on labeled satellite data. However, most existing Sentinel-2 datasets are cloud-free, which limits their usefulness in tropical regions where clouds are common. To properly evaluate the extent of this problem, we developed a cloud injection algorithm that simulates realistic cloud cover, allowing us to test how Sentinel-1 radar data can fill in the gaps caused by cloud-obstructed optical imagery. We also tackle the issue of losing spatial and/or spectral details during encoder downsampling in deep networks. To mitigate this loss, we propose a lightweight method that injects Normalized Difference Indices (NDIs) into the final decoding layers, enabling the model to retain key spatial features with minimal additional computation. Injecting NDIs enhanced land cover segmentation performance on the DFC2020 dataset, yielding improvements of 1.99% for U-Net and 2.78% for DeepLabV3 on cloud-free imagery. Under cloud-covered conditions, incorporating Sentinel-1 data led to significant performance gains across all models compared to using optical data alone, highlighting the effectiveness of radar-optical fusion in challenging atmospheric scenarios.
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