一种用于解决运行中Sentinel-2小麦面积估算中真实标签噪声的双轨策展与分类框架
A Dual-Track Curation-and-Classification Framework for Resolving Ground-Truth Label Noise in Operational Sentinel-2 Wheat Area Estimation
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- Space Applications Centre (SAC), Indian Space Research Organisation (ISRO)（印度空间研究组织空间应用中心）
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中文总结 AI 辅助
针对行政记录与遥感产品不一致问题，提出双轨策展与分类框架，利用策展参考集和XGBoost分类器，在低样本下实现高精度小麦面积估算，偏差仅+2.99%。
中文摘要 AI 辅助
运营性小麦种植面积估算持续受到行政记录与遥感分类产品之间不一致性的制约。我们针对印度旁遮普邦帕蒂亚拉区2022年拉比季的行政参考不一致问题，使用十三时间步的Sentinel-2 NDVI时间序列进行处理。通过迭代基于规则的引导程序开发的849样本策展参考数据集，支撑了特征敏感性分析和运营分类器。通过Cohen's d和梯度提升信息增益独立评估的特征敏感性，一致指向二月至三月的灌浆期窗口最具判别力。四种分类器（1D-CNN、LSTM、混合CNN-LSTM和XGBoost）在相同的679/170样本划分上进行了基准测试。XGBoost在深度学习基线（64-66%）中取得了最高总体准确率（78.82%），这与树集成在低样本场景中有利的参数与样本比率一致。在覆盖3625万有效区域像素的全人口部署中，运营分类器达到了86.31%的精确率和71.05%的召回率。预测的小麦范围与官方表格目标仅偏差+2.99%，而政府的空间参考掩膜相对于相同目标表现出+25.11%的正面积偏差。这种不对称性表明，基于审计策展参考集训练的分类器与官方表格面积的一致性，比通常用于验证它的空间产品更高。我们提出这种双轨策展与分类框架，作为标签噪声行政环境中作物面积协调的方法论参考。
英文摘要
Operational estimation of wheat-cultivated area is persistently constrained by discordance between administrative record-keeping and remotely sensed classification products. We address this administrative reference discordance for the 2022 Rabi season in Patiala district, Punjab, India, using a thirteen-timestep Sentinel-2 NDVI time series. A curated 849-sample reference dataset, developed through an iterative rule-based bootstrapping procedure, underpins both a feature sensitivity analysis and an operational classifier. Feature sensitivity independently assessed via Cohen's d and gradient-boosted information gain converges on the February-to-March grain-fill window as most discriminative. Four classifiers (1D-CNN, LSTM, hybrid CNN-LSTM, and XGBoost) were benchmarked on an identical 679/170 sample split. XGBoost achieved the highest overall accuracy (78.82%) against deep-learning baselines (64-66%), consistent with tree-based ensembles' favourable parameter-to-sample ratio in low-sample regimes. At full-population deployment across 36.25 million valid district pixels, the operational classifier attained 86.31% precision and 71.05% recall. The predicted wheat extent deviated by only +2.99% from the official tabular target, whereas the government's spatial reference mask exhibited a +25.11% positive area bias against the identical target. This asymmetry indicates that a classifier trained on an auditor-curated reference set reconciles more closely with the official tabular area than the spatial product conventionally used to validate it. We present this dual-track curation-and-classification framework as a methodological reference for crop-area reconciliation in label-noisy administrative settings.
