Graph Signal Processing for Heterogeneous Change Detection Part I: Vertex Domain Filtering
Yuli Sun, Lin Lei, Dongdong Guan, Gangyao Kuang, Li Liu

TL;DR
This paper introduces a graph signal processing approach for heterogeneous change detection in images, using vertex domain filtering to identify structural differences through graph-based analysis.
Contribution
It proposes a novel vertex domain filtering method for HCD, transforming images into graph signals and analyzing structural changes via graph filters.
Findings
Effective detection of structural changes demonstrated on seven datasets
Vertex domain filters capture high-order neighborhood information
Method outperforms traditional change detection techniques
Abstract
This paper provides a new strategy for the Heterogeneous Change Detection (HCD) problem: solving HCD from the perspective of Graph Signal Processing (GSP). We construct a graph for each image to capture the structure information, and treat each image as the graph signal. In this way, we convert the HCD into a GSP problem: a comparison of the responses of the two signals on different systems defined on the two graphs, which attempts to find structural differences (Part I) and signal differences (Part II) due to the changes between heterogeneous images. In this first part, we analyze the HCD with GSP from the vertex domain. We first show that for the unchanged images, their structures are consistent, and then the outputs of the same signal on systems defined on the two graphs are similar. However, once a region has changed, the local structure of the image changes, i.e., the connectivity…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Bioinformatics and Genomic Networks
