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.

翻译：变化检测（CD）是一种关键的遥感技术，用于识别地球表面随时间的变化。高光谱图像（HSI）出色的物质可识别性显著提升了检测精度，使得高光谱变化检测（HCD）成为一项重要技术。通过利用HSI的图结构可以进一步提升检测精度，这促使我们在解决HCD问题时采用图神经网络（GNN）。本研究首次将量子深度网络（QUEEN）引入HCD领域。与GNN和CNN均提取仿射计算特征不同，QUEEN提供了本质不同的酉计算特征。我们证明，通过酉特征提取过程，QUEEN为判定是否发生变化提供了全新的信息。在层次结构上，图特征学习（GFL）模块在超像素级别利用双时相HSI的图结构，而量子特征学习（QFL）模块在像素级别学习量子特征，作为GFL的补充，以保留超像素中未能保留的像素级详细空间信息。在最终分类阶段，设计了一个量子分类器与传统全连接分类器协同工作。所提出的QUEEN赋能GNN（即QUEEN-G）的优越HCD性能将在真实高光谱数据集上通过实验得到验证。