Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous data distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper presents a novel architecture which explicitly addresses these two issues. Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces. In each spectral embedding space, we propose graph attention mechanism to explicitly perform interpretable reasoning in the spatial domain based on the connection in spectral feature space. Experiments on three HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy compared with the existing methods.

中文翻译：

---

用于高光谱图像分类的光谱金字塔图注意网络

卷积神经网络 (CNN) 在高光谱图像 (HSI) 分类方面取得了重大进展。然而，标准卷积核忽略了数据点之间的内在联系，导致区域描绘不佳和虚假预测很小。此外，HSI 在高维频谱域上具有独特的连续数据分布——考虑到高维数和有限的标记数据量，在表征频谱上下文方面仍有许多需要解决的问题。本文提出了一种新颖的架构，它明确地解决了这两个问题。具体来说，我们设计了一种架构，以多个嵌入空间的频谱金字塔的形式对多个频谱上下文信息进行编码。在每个光谱嵌入空间中，我们提出了图注意力机制，以基于光谱特征空间中的连接在空间域中明确地执行可解释推理。在三个 HSI 数据集上的实验表明，与现有方法相比，所提出的架构可以显着提高分类精度。