Interpretation of large-scale aerial imagery is an essential challenge due to the dense distribution of objects and complex intra-class backgrounds. Non-local relational modeling has been adopted as the mainstream solution to better interpret such large-scale scenes. Existing non-local relations modeling methods are based on building deep convolution neural networks, attention mechanisms, or deep graph neural networks for all positions of the whole image. However, the existing methods are computationally expensive and inefficient. In this paper, we dig into the main causes of such inefficiency and find two main disadvantages of existing methods, i.e., (1) semantic relations are blurry and homogeneous, (2) insufficient construction of high-order relations. To overcome the above issues, we analyze the inadequacy of traditional hypergraph definition and propose a lightweight hypergraph construction strategy to learn the non-local relations. The proposed method can model high-order relations more effectively and lead to an explicit representation of semantic information. We apply this strategy in the spatial dimension and construct a fully-weighted hypergraph neural network (HGNN) to capture short- and long-range dependencies in such a large-scale aerial image. Furthermore, we design a hypergraph convolutional feature pyramid network (Hyper-FPN), which learns the non-local relations in multi-scale features and then aggregates hierarchical global contexts. Extensive experiments on geospatial visual recognition demonstrate that Hyper-FPN significantly improves the performance under our strategy. Moreover, our approach can be easily embedded into state-of-the-art (SOTA) architectures to achieve higher performance.

由于对象的密集分布和复杂的类内背景，对大规模航空图像的解释是一项基本挑战。采用非局部关系建模作为主流解决方案来更好地解释这种大规模场景。现有的非局部关系建模方法基于为整个图像的所有位置构建深度卷积神经网络、注意力机制或深度图神经网络。然而，现有的方法计算成本高且效率低。在本文中，我们深入挖掘了这种低效率的主要原因，并发现了现有方法的两个主要缺点，即（1）语义关系模糊且同质，（2）高阶关系构建不足。为克服上述问题，我们分析了传统超图定义的不足，提出了一种轻量级的超图构建策略来学习非局部关系。所提出的方法可以更有效地建模高阶关系并导致语义信息的显式表示。我们在空间维度上应用这种策略，并构建一个全权重的超图神经网络（HGNN）来捕捉如此大规模航拍图像中的短程和长程依赖关系。此外，我们设计了一个超图卷积特征金字塔网络（Hyper-FPN），它学习多尺度特征中的非局部关系，然后聚合分层的全局上下文。地理空间视觉识别的大量实验表明，Hyper-FPN 在我们的策略下显着提高了性能。而且，