AI 中文总结
本研究提出首个仅用SAR强度影像的自监督学习流水线，通过改进时间预文本任务提升物候特征捕捉能力，在SICKLE基准上的作物类型制图任务中，其IoU较光学基线和现有SAR基线均有显著提升，验证了SAR强度编码器预训练的有效性。
AI 中文摘要
农业监测面临独特挑战，源于景观复杂的时间、物候及气候动态，而监测这些对保障粮食安全至关重要。合成孔径雷达（SAR）卫星具备全天时、全天候成像能力，支持作物类型制图、产量预测及物候事件检测等关键监测任务。现有多模态遥感基础模型包括TerraMind和CopernicusFM，通过联合编码与对比学习技术将SAR表征与光学影像关联学习；而SAR专用基础模型如SAR-JEPA、SARMAE、SAR-W-MixMAE主要聚焦目标检测、洪水制图及土地覆盖分类任务。近期研究引入受物候启发的基于光学影像的时间预文本任务，在农业下游任务中表现出色。本研究提出首个仅利用SAR强度影像的自监督学习流水线用于农业应用，通过掩码与课程学习改进时间预文本任务，提升预训练流水线从SAR中捕捉物候特征的能力。在SICKLE基准上，最终模型在作物类型制图任务中达到84.9%的IoU，较光学基线提升15.3个百分点，较现有SAR基线提升2.2个百分点，验证了所提流水线用于农业监测的SAR强度编码器预训练的有效性。
英文摘要
Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection. Existing multimodal remote sensing foundation models including TerraMind and CopernicusFM learn SAR representations by grounding them in optical imagery using joint encoding and contrastive learning techniques, while SAR-specific foundation models such as SAR-JEPA, SARMAE, and SAR-W-MixMAE primarily focus on target detection, flood mapping, and land cover classification applications. Recent work has introduced phenology inspired temporal pretext tasks with optical imagery which has shown strong performance on agricultural downstream tasks. In this work, we propose the first self-supervised learning pipeline focused on using only SAR intensity imagery for agricultural applications. We improve the temporal pretext tasks through masking and curriculum learning to enhance the pretraining pipeline's ability to capture phenological features from SAR. On the SICKLE benchmark, our final model achieves 84.9% IoU on crop type mapping, outperforming optical baselines (by 15.3 pt) and existing SAR baselines (by 2.2 pt), demonstrating the effectiveness of our proposed pipeline for pretraining SAR intensity encoders for agricultural monitoring.
