# A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images

arXiv: https://arxiv.org/abs/2202.13275 · HTML: https://mlsanity.com/paper/2202.13275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 27 Feb 2022.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://mlsanity.com/paper/1705.07664/) (2017)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.06133/) (2019)
- [Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network](https://mlsanity.com/paper/1912.08628/) (2019)
- [AEGCN: An Autoencoder-Constrained Graph Convolutional Network](https://mlsanity.com/paper/2007.03424/) (2020)
- [Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net](https://mlsanity.com/paper/1906.07923/) (2019)

## Abstract (arXiv)

The very high spatial resolution (VHR) remote sensing images have been an extremely valuable source for monitoring changes occurred on the earth surface. However, precisely detecting relevant changes in VHR images still remains a challenge, due to the complexity of the relationships among ground objects. To address this limitation, a dual neighborhood hypergraph neural network is proposed in this article, which combines the multiscale superpixel segmentation and hypergraph convolution to model and exploit the complex relationships. First, the bi-temporal image pairs are segmented under two scales and fed to a pre-trained U-net to obtain node features by treating each object under the fine scale as a node. The dual neighborhood is then defined using the father-child and adjacent relationships of the segmented objects to construct the hypergraph, which permits models to represent the higher-order structured information far more complex than just pairwise relationships. The hypergraph convolutions are conducted on the constructed hypergraph to propagate the label information from a small amount of labeled nodes to the other unlabeled ones by the node-edge-node transform. Moreover, to alleviate the problem of imbalanced sample, the focal loss function is adopted to train the hypergraph neural network. The experimental results on optical, SAR and heterogeneous optical/SAR data sets demonstrate that the proposed method comprises better effectiveness and robustness compared to many state-of-the-art methods.