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

08/03/2022
by   Yuli Sun, et al.
0

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.

READ FULL TEXT

page 3

page 10

page 13

research
08/03/2022

Graph Signal Processing for Heterogeneous Change Detection Part II: Spectral Domain Analysis

This is the second part of the paper that provides a new strategy for th...
research
09/23/2019

Graph Signal Processing – Part II: Processing and Analyzing Signals on Graphs

The focus of Part I of this monograph has been on both the fundamental p...
research
11/16/2022

Graph Filters for Signal Processing and Machine Learning on Graphs

Filters are fundamental in extracting information from data. For time se...
research
02/14/2016

Autoregressive Moving Average Graph Filtering

One of the cornerstones of the field of signal processing on graphs are ...
research
06/18/2020

Offline detection of change-points in the mean for stationary graph signals

This paper addresses the problem of segmenting a stream of graph signals...
research
12/07/2022

A Frequency-Structure Approach for Link Stream Analysis

A link stream is a set of triplets (t, u, v) indicating that u and v int...
research
01/23/2018

Signal Subgraph Estimation Via Vertex Screening

Graph classification and regression have wide applications in a variety ...

Please sign up or login with your details

Forgot password? Click here to reset