用雷达光学融合时序模型精准识别陕北油松林，准确率达99.67%。
ROSA-TFormer: A Radar-Optical Sensor-Aware Temporal Transformer for Pinus sylvestris Plantation Classification in Northern Shaanxi Using GEE-Derived Sentinel-1/2 Time Series
- 设计雷达光学双分支时序注意力网络，融合多源遥感数据
- 在半月尺度数据上达到99.67%整体准确率，油松分类F1达98.91%
- 适合高精度生态修复监测，尤其适用于点级林地分类任务
精确识别陕北地区油松人工林对监测造林质量与生态恢复至关重要。本文提出ROSA-TFormer，一种基于谷歌地球引擎生成的哨兵-1/2时序数据的雷达-光学感知时序变换器，用于油松林分类。该模型采用独立的合成孔径雷达（SAR）与光学嵌入分支、传感器感知门控机制及时间注意力池化，有效捕捉多源季节性特征。在月度与半月尺度点级数据集上的实验表明，ROSA-TFormer表现优异：在HalfMonth-dataBig数据集上，整体准确率达99.67%，宏平均F1为99.56%，油松类别F1为98.91%。空间块验证与消融实验进一步证明雷达-光学时序融合与传感器感知建模的有效性。结果表明ROSA-TFormer具备点级油松林分类潜力，但更广泛全覆盖验证仍需开展。
原文摘要 · Abstract (English)
Accurate identification of Pinus sylvestris var. mongolica plantations is important for monitoring afforestation quality and ecological restoration in northern Shaanxi. This paper proposes ROSA-TFormer, a radar-optical sensor-aware temporal Transformer for P. sylvestris classification using Sentinel-1/2 time-series data generated on Google Earth Engine. The model integrates separate SAR and optical embedding branches, a sensor-aware gate, and temporal attention pooling to capture multi-source seasonal features. Experiments on monthly and half-month point-level datasets show that ROSA-TFormer achieves strong classification performance, with 99.67% overall accuracy, 99.56% macro F1, and 98.91% P. sylvestris F1 on the HalfMonth-dataBig dataset. Spatial block validation and ablation results further indicate the effectiveness of radar-optical temporal fusion and sensor-aware modeling. The results demonstrate the potential of ROSA-TFormer for point-level P. sylvestris plantation classification, while broader wall-to-wall validation remains necessary.
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