用雷达回波强度加权改进雷达图像预训练，提升洪水检测效果
SAR-W-MixMAE: SAR Foundation Model Training Using Backscatter Power Weighting
- 基于雷达回波强度设计重建损失权重，抑制斑点噪声影响
- 在哨兵-1雷达数据上预训练，洪水检测准确率显著优于基线
- 为遥感雷达图像构建基础模型提供有效新方法，适合遥感领域研究者
以掩码自编码器（MAE）为代表的预训练方法正被成功应用于卫星影像。目前大多数验证集中于可见光或多光谱图像，而合成孔径雷达（SAR）因语义标注困难且噪声更高，尚未被广泛用于基础模型研究。本文探索了在哨兵-1 SAR影像上应用MixMAE进行预训练，并提出利用SAR数据的物理特性，在自编码器训练损失（均方误差）中引入回波强度加权机制，以降低斑点噪声和极端高值的影响。实验表明，该加权策略在雷达图像预训练及下游任务（尤其是洪水检测）中表现优异，显著优于基准模型。
原文摘要 · Abstract (English)
Foundation model approaches such as masked auto-encoders (MAE) or its variations are now being successfully applied to satellite imagery. Most of the ongoing technical validation of foundation models have been applied to optical images like RGB or multi-spectral images. Due to difficulty in semantic labeling to create datasets and higher noise content with respect to optical images, Synthetic Aperture Radar (SAR) data has not been explored a lot in the field for foundation models. Therefore, in this work as a pre-training approach, we explored masked auto-encoder, specifically MixMAE on Sentinel-1 SAR images and its impact on SAR image classification tasks. Moreover, we proposed to use the physical characteristic of SAR data for applying weighting parameter on the auto-encoder training loss (MSE) to reduce the effect of speckle noise and very high values on the SAR images. Proposed SAR intensity-based weighting of the reconstruction loss demonstrates promising results both on SAR pre-training and downstream tasks specifically on flood detection compared with the baseline model.
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