用自监督方法提升雷达图像语义理解能力
SARMAE: Masked Autoencoder for SAR Representation Learning
- 构建带斑点噪声的掩码自编码器，增强对雷达特性的学习
- 在百万级遥感数据集上训练，分类检测分割均达顶尖水平
- 适合遥感、军事、灾害监测等需要全天候成像的领域
合成孔径雷达（SAR）图像在全天候、全天时遥感中至关重要，但现有深度学习受限于数据稀缺，且物理层面的斑点噪声阻碍了细粒度语义表示学习。为此，我们提出SARMAE，一种面向自监督SAR表征学习的噪声感知掩码自编码器。首先构建首个百万级SAR数据集SAR-1M，包含配对光学图像，支持大规模预训练；在此基础上，设计斑点感知表征增强（SARE），将特定于SAR的斑点噪声注入掩码自编码器，促进噪声鲁棒的表征学习；进一步引入语义锚定表征约束（SARC），利用配对光学先验对齐SAR特征，保证语义一致性。在多个SAR数据集上的大量实验表明，SARMAE在分类、检测和分割任务中均达到当前最优性能。代码与模型将在https://github.com/MiliLab/SARMAE发布。
原文摘要 · Abstract (English)
Synthetic Aperture Radar (SAR) imagery plays a critical role in all-weather, day-and-night remote sensing applications. However, existing SAR-oriented deep learning is constrained by data scarcity, while the physically grounded speckle noise in SAR imagery further hampers fine-grained semantic representation learning. To address these challenges, we propose SARMAE, a Noise-Aware Masked Autoencoder for self-supervised SAR representation learning. Specifically, we construct SAR-1M, the first million-scale SAR dataset, with additional paired optical images, to enable large-scale pre-training. Building upon this, we design Speckle-Aware Representation Enhancement (SARE), which injects SAR-specific speckle noise into masked autoencoders to facilitate noise-aware and robust representation learning. Furthermore, we introduce Semantic Anchor Representation Constraint (SARC), which leverages paired optical priors to align SAR features and ensure semantic consistency. Extensive experiments across multiple SAR datasets demonstrate that SARMAE achieves state-of-the-art performance on classification, detection, and segmentation tasks. Code and models will be available at https://github.com/MiliLab/SARMAE.
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