用插值法增强甘蔗田杂草数据，提升模型预测效果
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion
- 用高斯过程和克里金法插值地理数据，扩充样本
- 组合核函数的高斯过程使模型性能提升，所需新增数据少
- 适合数据稀缺的农业遥感场景，尤其关注空间一致性
数据增强是构建稳健监督学习模型的关键步骤，尤其在数据量有限时。本研究探索了针对地理定位数据的插值技术，旨在预测留尼汪甘蔗田中布氏飘萍（Commelina benghalensis L.）的分布。鉴于数据的空间特性及采集成本高昂，评估了两种插值方法：使用不同核函数的高斯过程（GPs）以及采用多种变差函数的克里金法。研究目标包括：(i) 确定对各类回归算法表现最佳的插值方法；(ii) 分析随着新增观测数量增加，模型性能的变化趋势；(iii) 评估增强数据集的空间一致性。结果表明，基于高斯过程的方法（特别是组合核函数，即GP-COMB）显著提升了回归算法性能，且所需额外数据更少。尽管克里金法性能略低，但其空间覆盖更均匀，在某些场景下具有优势。
原文摘要 · Abstract (English)
Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La Réunion. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts.
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