Convolutional neural networks (CNNs) have achieved remarkable performance in hyperspectral image (HSI) classification over the last few years. Despite the progress that has been made, rich and informative spectral information of HSI has been largely underutilized by existing methods which employ convolutional kernels with limited size of receptive field in the spectral domain. To address this issue, we propose a spectral graph reasoning network (SGR) learning framework comprising two crucial modules: 1) a spectral decoupling module which unpacks and casts multiple spectral embeddings into a unified graph whose node corresponds to an individual spectral feature channel in the embedding space; the graph performs interpretable reasoning to aggregate and align spectral information to guide learning spectral-specific graph embeddings at multiple contextual levels 2) a spectral ensembling module explores the interactions and interdependencies across graph embedding hierarchy via a novel recurrent graph propagation mechanism. Experiments on two HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy compared with the existing methods with a sizable margin.

翻译：卷积神经网络（CNN）近年来在高光谱图像（HSI）分类中取得了显著性能。尽管已有进展，现有方法在光谱域中使用感受野有限的卷积核，导致HSI丰富且信息量大的光谱信息未能得到充分利用。为解决这一问题，我们提出了一种光谱图推理网络（SGR）学习框架，包含两个关键模块：1）光谱解耦模块，将多个光谱嵌入解耦并映射到统一图中，图中每个节点对应嵌入空间中的单个光谱特征通道；该图通过可解释的推理聚合并对齐光谱信息，以指导在多上下文层次学习光谱特定的图嵌入；2）光谱集成模块，通过一种新颖的循环图传播机制探索图嵌入层次间的相互作用与相互依赖关系。在两个HSI数据集上的实验表明，所提架构相比现有方法能够显著提升分类准确率，且优势明显。