PhenoStitch：基于卫星图像时间序列的无需训练的全景作物制图
PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series
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中文总结 AI 辅助
PhenoStitch是无需特定任务梯度训练的全景作物制图流程，结合无标注地块划分与小样本物候识别，在每类仅20个标注的有限监督下，优于多种基线方法
中文摘要 AI 辅助
全景作物制图需要从卫星图像时间序列中同时划分出单个农业地块并为每个地块分配作物类型。现有方法通常依赖密集的地块级标注和特定任务的模型训练，这限制了它们在新地区和生长季节的适用性。我们提出了PhenoStitch，一种无需特定任务基于梯度训练的全景作物制图流程。首先，冻结的Segment Anything模型将每个图像块过分割为类别无关的区域；对于每个区域，光学NDVI和Sentinel-1后向散射时间序列通过解析双谐波物候特征进行汇总；随后通过最小化Potts图能量将相邻区域合并为地块，每个地块仅使用每类（k）个标注地块通过最近原型匹配进行分类；最后通过拓扑闭合步骤生成全景图。在每类（k=20）个标注地块的匹配预算下，对应不到可用标注的1%，PhenoStitch在PASTIS-R数据集的5折3种子评估中，作物mIoU达到20.0，分割质量为76.2，全景质量为6.2。在相同协议下，它优于评估的冻结基础模型、小样本和匹配预算的监督基线，在ZueriCrop上也观察到一致的排名。 ablation研究显示，雷达观测贡献了最大的性能提升，而图能量合并和紧凑的物候特征进一步提升了性能。这些结果表明，将无标注的地块划分与小样本物候识别相结合，在有限监督下进行全景作物制图是有效的。
英文摘要
Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series. Existing approaches typically rely on dense parcel-level annotations and task-specific model training, which limits their applicability to new regions and growing seasons. We introduce PhenoStitch, a panoptic crop-mapping pipeline that requires no task-specific gradient-based training. A frozen Segment Anything model first oversegments each patch into class-agnostic regions. For each region, optical NDVI and Sentinel-1 backscatter series are summarized by an analytic double-harmonic phenological signature. Adjacent regions are then merged into parcels by minimizing a Potts graph energy, and each parcel is classified by nearest-prototype matching using only (k) labeled parcels per class. A final topology-closure step produces the panoptic map. Under a matched budget of (k=20) parcels per class, corresponding to less than 1% of the available labels, PhenoStitch achieves 20.0 crop mIoU, 76.2 segmentation quality, and 6.2 panoptic quality on PASTIS-R under a 5-fold, 3-seed evaluation. It outperforms the evaluated frozen foundation-model, few-shot, and matched-budget supervised baselines under the same protocol, with a consistent ranking also observed on ZueriCrop. Ablation studies show that radar observations contribute the largest performance gain, while the graph-energy merge and compact phenological signature provide further improvements. These results demonstrate the effectiveness of combining label-free parcel delineation with few-shot phenological recognition for panoptic crop mapping under limited supervision.
