论文标题
探索自我发挥作物型分类的解释性
Exploring Self-Attention for Crop-type Classification Explainability
论文作者
论文摘要
变压器模型已成为农作物型分类的一种有希望的方法。尽管他们的注意力权重可以用来了解作物歧义的相关时间点，但这些见解的有效性取决于注意力权重近似这些黑盒模型的实际工作原理，这并不总是很清楚。在本文中，我们介绍了一个新颖的解释性框架，该框架系统地评估了用于作物型分类的标准变压器编码器的注意力权重的解释力。我们的结果表明，注意模式与关键日期密切相关，这些日期通常与用于作物型分类的关键物候事件有关。此外，敏感性分析揭示了注意力权重表征作物物候的能力有限，因为已确定的物候事件取决于训练期间考虑的其他农作物。这一限制强调了未来工作与能够自动学习暂时植被动态的深度学习方法的相关性
Transformer models have become a promising approach for crop-type classification. Although their attention weights can be used to understand the relevant time points for crop disambiguation, the validity of these insights depends on how closely the attention weights approximate the actual workings of these black-box models, which is not always clear. In this paper, we introduce a novel explainability framework that systematically evaluates the explanatory power of the attention weights of a standard transformer encoder for crop-type classification. Our results show that attention patterns strongly relate to key dates, which are often associated with critical phenological events for crop-type classification. Further, the sensitivity analysis reveals the limited capability of the attention weights to characterize crop phenology as the identified phenological events depend on the other crops considered during training. This limitation highlights the relevance of future work towards the development of deep learning approaches capable of automatically learning the temporal vegetation dynamics for accurate crop disambiguation
