With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanced - fusion strategies may outperform this traditional approach. This work assesses different fusion strategies for crop classification in the CropHarvest dataset. The fusion methods proposed in this work outperform models based on individual views and previous fusion methods. We do not find one single fusion method that consistently outperforms all other approaches. Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance. Despite this, we suggest a preliminary criterion for the selection of fusion methods.

翻译：随着遥感数据源数量和多样性的快速增长，多视角学习建模的需求日益迫切。考虑到遥感数据在分辨率、量级和噪声方面的差异，这是一项复杂的任务。通常合并多个遥感源的方法是输入级融合，但其他更先进的融合策略可能优于这种传统方法。本研究评估了CropHarvest数据集中用于作物分类的不同融合策略。本文提出的融合方法优于基于单一视角的模型和以往的融合方法。我们没有发现一种单一的融合方法能始终优于所有其他方法。相反，我们针对三个不同数据集呈现了多视角融合方法的比较，结果表明，根据测试区域的不同，不同方法可获得最佳性能。尽管如此，我们仍提出了一个初步的融合方法选择标准。