Graph Signal Processing for Heterogeneous Change Detection Part I: Vertex Domain Filtering

• URL: http://arxiv.org/abs/2208.01881v1
• Date: Wed, 3 Aug 2022 07:22:45 GMT
• Title: Graph Signal Processing for Heterogeneous Change Detection Part I: Vertex Domain Filtering
• Authors: Yuli Sun, Lin Lei, Dongdong Guan, Gangyao Kuang, Li Liu
• Abstract summary: This paper provides a new strategy for the Heterogeneous Change Detection 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.
• Score: 21.531426428400227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 of the vertex containing this region changes. Then, we can compare the output signals of the same input graph signal passing through filters defined on the two graphs to detect changes. We design different filters from the vertex domain, which can flexibly explore the high-order neighborhood information hidden in original graphs. We also analyze the detrimental effects of changing regions on the change detection results from the viewpoint of signal propagation. Experiments conducted on seven real data sets show the effectiveness of the vertex domain filtering based HCD method.

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