研究多源遥感数据缺失时模型表现，发现删掉某些数据反能提升预测效果。
On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
- 对比六种先进模型在单源缺失或仅单源可用时的表现
- 发现模型有效性取决于任务性质、数据源互补性与设计结构
- 挑战‘数据越多越好’假设，适合关注模型精简的遥感研究者
近年来，地球观测（EO）领域涌现出多种鲁棒的多源模型，利用多元数据提升在数据缺失情况下的预测精度。尽管如此，影响这些模型效能差异的关键因素仍不明确。本研究评估了六种前沿多源模型在单源缺失或仅保留单一数据源条件下的预测性能。分析表明，模型有效性高度依赖于任务特性、数据源间的互补性以及模型架构设计。令人意外的是，某些情况下移除特定数据源反而提升了预测表现，挑战了‘整合所有可用数据始终有益’的普遍假设。该发现促使我们重新思考模型复杂度与数据收集必要性，可能推动地球观测应用中更高效、简洁的建模方式。
原文摘要 · Abstract (English)
In recent years, the development of robust multi-source models has emerged in the Earth Observation (EO) field. These are models that leverage data from diverse sources to improve predictive accuracy when there is missing data. Despite these advancements, the factors influencing the varying effectiveness of such models remain poorly understood. In this study, we evaluate the predictive performance of six state-of-the-art multi-source models in predicting scenarios where either a single data source is missing or only a single source is available. Our analysis reveals that the efficacy of these models is intricately tied to the nature of the task, the complementarity among data sources, and the model design. Surprisingly, we observe instances where the removal of certain data sources leads to improved predictive performance, challenging the assumption that incorporating all available data is always beneficial. These findings prompt critical reflections on model complexity and the necessity of all collected data sources, potentially shaping the way for more streamlined approaches in EO applications.
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