Locality Preservation for Unsupervised Multimodal Change Detection in Remote Sensing Imagery
Yuli Sun, Lin Lei, Dongdong Guan, Gangyao Kuang, Zhang Li, Li Liu
Abstract
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.