Detecting land use changes in urban areas from very-high-resolution (VHR) satellite images presents two primary challenges: 1) traditional methods focus mainly on comparing changes in land cover-related features, which are insufficient for detecting changes in land use and are prone to pseudo-changes caused by illumination differences, seasonal variations, and subtle structural changes and 2) spatial structural information, which is characterized by topological relationships among land cover objects, is crucial for urban land use classification but remains underexplored in change detection. To address these challenges, this study developed a local-global structural interaction network (LGSI-Net) based on a Siamese graph neural network (SGNN) that integrates high-level structural and semantic information to detect urban land use changes from bitemporal VHR images. We developed both local structural feature interaction module (LSIM) and global structural feature interaction module (GSIM) to enhance the representation of bitemporal structural features at the global scene graph and local object node levels. Experiments on the publicly available MtS-WH dataset and two generated datasets, LUCD-FZ and LUCD-HF, show that the proposed method outperforms the existing bag of visual word (BoVW)-based method and CorrFusionNet. Furthermore, we evaluated the detection performance for different semantic feature extraction strategies and structural feature extraction backbones. The results demonstrate that the proposed method, which integrates high-level semantic and graph isomorphism network (GIN)-derived structural features achieves the best performance. The method trained on the LUCD-FZ dataset was successfully transferred to the LUCD-HF dataset with different urban landscapes, indicating its effectiveness in detecting land use changes from VHR satellite images, even in areas with relatively large imbalances between changed and unchanged samples.

中文翻译：

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使用 Siamese 图神经网络集成局部-全局结构相互作用，从 VHR 卫星图像中检测城市土地利用变化


从超高分辨率（VHR）卫星图像中检测城市地区的土地利用变化面临两个主要挑战：1）传统方法主要侧重于比较土地覆盖相关特征的变化，这不足以检测土地利用的变化，而且容易出现由光照差异、季节变化和微妙的结构变化引起的伪变化；2）空间结构信息，以土地覆盖对象之间的拓扑关系为特征，对于城市土地利用分类至关重要，但在变化检测中仍未得到充分探索。为了应对这些挑战，本研究开发了一种基于暹罗图神经网络（SGNN）的局部-全局结构交互网络（LGSI-Net），该网络集成了高级结构和语义信息，以从双时态 VHR 图像中检测城市土地利用变化。我们开发了局部结构特征交互模块（LSIM）和全局结构特征交互模块（GSIM），以增强全局场景图和局部对象节点级别的双时态结构特征的表示。在公开可用的 MtS-WH 数据集和两个生成的数据集 LUCD-FZ 和 LUCD-HF 上的实验表明，所提出的方法优于现有的基于视觉词包 (BoVW) 的方法和 CorrFusionNet。此外，我们评估了不同语义特征提取策略和结构特征提取主干的检测性能。结果表明，所提出的方法集成了高级语义和图同构网络（GIN）衍生的结构特征，实现了最佳性能。 在 LUCD-FZ 数据集上训练的方法成功地转移到具有不同城市景观的 LUCD-HF 数据集，表明其在从 VHR 卫星图像中检测土地利用变化方面是有效的，即使在变化样本和未变化样本之间不平衡性相对较大的区域也是如此。