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

TL;DR

This paper introduces a dual neighborhood hypergraph neural network that leverages multiscale segmentation and hypergraph convolution to improve change detection in very high resolution remote sensing images, capturing complex relationships among ground objects.

Contribution

It proposes a novel hypergraph neural network model that models higher-order relationships using dual neighborhood hypergraphs and combines it with multiscale segmentation for enhanced change detection.

Findings

Outperforms state-of-the-art methods on optical, SAR, and heterogeneous datasets.

Effectively models complex relationships among objects in VHR images.

Demonstrates robustness and improved accuracy in change detection tasks.

Abstract

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.