Multi-view learning (MVL) leverages multiple sources or views of data to enhance machine learning model performance and robustness. This approach has been successfully used in the Earth Observation (EO) domain, where views have a heterogeneous nature and can be affected by missing data. Despite the negative effect that missing data has on model predictions, the ML literature has used it as an augmentation technique to improve model generalization, like masking the input data. Inspired by this, we introduce novel methods for EO applications tailored to MVL with missing views. Our methods integrate the combination of a set to simulate all combinations of missing views as different training samples. Instead of replacing missing data with a numerical value, we use dynamic merge functions, like average, and more complex ones like Transformer. This allows the MVL model to entirely ignore the missing views, enhancing its predictive robustness. We experiment on four EO datasets with temporal and static views, including state-of-the-art methods from the EO domain. The results indicate that our methods improve model robustness under conditions of moderate missingness, and improve the predictive performance when all views are present. The proposed methods offer a single adaptive solution to operate effectively with any combination of available views.

本研究解决了地球观测领域在多视角学习中因缺失数据而影响模型性能的问题。提出的新方法通过模拟缺失视角的组合，使用动态合并函数而非简单的数据填充，显著增强了模型的预测鲁棒性。实验结果表明，该方法在适度缺失条件下提升了模型的鲁棒性，且在所有视角可用时也提高了预测性能。

地球观测领域中缺失数据作为增强的多视角学习方法