Knowledge Graph-Guided Deep Network for Hyperspectral Remote Sensing Image Classification

For the classification of hyperspectral images (HSIs), most deep learning networks are data-driven and lack the usage of prior knowledge. In this letter, we propose a knowledge graph-guided classification network (KGNet), attempting to utilize the prior knowledge of land cover categories to enhance the classification performance. We first construct a knowledge graph on several hyperspectral scenes, which can characterize not only the attributes of land cover categories but also the rich connections between categories. Semantic features are then derived to represent the knowledge in the graph. Knowledge-guided learning is achieved by performing feature alignment between semantic and visual features. Finally, classification is performed on visual features that have contained the knowledge from semantic features. Experiments on three datasets demonstrate the effectiveness of applying the knowledge graph for the classification of hyperspectral remote sensing images.