Sun Yuli, Lei Lin, Guan Dongdong, Kuang Gangyao, Li Zhang, Liu Li
IEEE Trans Neural Netw Learn Syst. 2025 Apr;36(4):6955-6969. doi: 10.1109/TNNLS.2024.3401696. Epub 2025 Apr 4.
Multimodal change detection (MCD) is a topic of increasing interest in remote sensing. Due to different imaging mechanisms, the multimodal images cannot be directly compared to detect the changes. In this article, we explore the topological structure of multimodal images and construct the links between class relationships (same/different) and change labels (changed/unchanged) of pairwise superpixels, which are imaging modality-invariant. With these links, we formulate the MCD problem within a mathematical framework termed the locality-preserving energy model (LPEM), which is used to maintain the local consistency constraints embedded in the links: the structure consistency based on feature similarity and the label consistency based on spatial continuity. Because the foundation of LPEM, i.e., the links, is intuitively explainable and universal, the proposed method is very robust across different MCD situations. Noteworthy, LPEM is built directly on the label of each superpixel, so it is a paradigm that outputs the change map (CM) directly without the need to generate intermediate difference image (DI) as most previous algorithms have done. Experiments on different real datasets demonstrate the effectiveness of the proposed method. Source code of the proposed method is made available at https://github.com/yulisun/LPEM.
多模态变化检测（MCD）是遥感领域中一个日益受到关注的话题。由于成像机制不同，多模态图像不能直接进行比较以检测变化。在本文中，我们探索了多模态图像的拓扑结构，并构建了成对超像素的类关系（相同/不同）与变化标签（已变化/未变化）之间的联系，这些联系是成像模态不变的。利用这些联系，我们在一个称为局部保持能量模型（LPEM）的数学框架内制定了MCD问题，该模型用于维持嵌入在这些联系中的局部一致性约束：基于特征相似性的结构一致性和基于空间连续性的标签一致性。由于LPEM的基础，即这些联系，在直观上是可解释且通用的，所以所提出的方法在不同的MCD情况下非常稳健。值得注意的是，LPEM直接基于每个超像素的标签构建，因此它是一种直接输出变化图（CM）的范式，无需像大多数先前算法那样生成中间差分图像（DI）。在不同真实数据集上的实验证明了所提方法的有效性。所提方法的源代码可在https://github.com/yulisun/LPEM获取。
