Change detection (CD) is a critical remote sensing technique for identifying changes in the Earth's surface over time. The outstanding substance identifiability of hyperspectral images (HSIs) has significantly enhanced the detection accuracy, making hyperspectral change detection (HCD) an essential technology. The detection accuracy can be further upgraded by leveraging the graph structure of HSIs, motivating us to adopt the graph neural networks (GNNs) in solving HCD. For the first time, this work introduces quantum deep network (QUEEN) into HCD. Unlike GNN and CNN, both extracting the affine-computing features, QUEEN provides fundamentally different unitary-computing features. We demonstrate that through the unitary feature extraction procedure, QUEEN provides radically new information for deciding whether there is a change or not. Hierarchically, a graph feature learning (GFL) module exploits the graph structure of the bitemporal HSIs at the superpixel level, while a quantum feature learning (QFL) module learns the quantum features at the pixel level, as a complementary to GFL by preserving pixel-level detailed spatial information not retained in the superpixels. In the final classification stage, a quantum classifier is designed to cooperate with a traditional fully connected classifier. The superior HCD performance of the proposed QUEEN-empowered GNN (i.e., QUEEN-G) will be experimentally demonstrated on real hyperspectral datasets.

该研究解决了高光谱变化检测（HCD）中检测精度不足的问题。通过首次引入量子深度网络（QUEEN），该研究提供了独特的单位计算特征，与传统的图神经网络（GNN）和卷积神经网络（CNN）不同。研究表明，QUEEN-G在实际高光谱数据集上的变化检测性能显著提升，为遥感技术的发展开辟了新途径。

量子信息赋能的图神经网络用于高光谱变化检测