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

arXiv: https://arxiv.org/abs/2208.01881 · HTML: https://mlsanity.com/paper/2208.01881/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 84 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Discrete Signal Processing on Graphs: Frequency Analysis](https://mlsanity.com/paper/1307.0468/) (2013)
- [Self-supervised Change Detection in Multi-view Remote Sensing Images](https://mlsanity.com/paper/2103.05969/) (2021)
- [Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection](https://mlsanity.com/paper/2001.04271/) (2020)
- [Unsupervised Image Regression for Heterogeneous Change Detection](https://mlsanity.com/paper/1909.05948/) (2019)
- [Self-Supervised Multisensor Change Detection](https://mlsanity.com/paper/2103.05102/) (2021)
- [A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2102.08041/) (2021)
- [Change Detection in Heterogeneous Optical and SAR Remote Sensing Images via Deep Homogeneous Feature Fusion](https://mlsanity.com/paper/2004.03830/) (2020)
- [Adaptive Local Structure Consistency based Heterogeneous Remote Sensing Change Detection](https://mlsanity.com/paper/2008.12958/) (2020)
- [Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective](https://mlsanity.com/paper/1601.00088/) (2016)

## Built on by (most cited first, 4 of 4)

- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2303.17448/) (2023)
- [Semantic Change Detection of Roads and Bridges: A Fine-grained Dataset and Multimodal Frequency-driven Detector](https://mlsanity.com/paper/2505.13212/) (2025)
- [ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection](https://mlsanity.com/paper/2607.07161/) (2026)

## Abstract (arXiv)

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.