AGCD: An Attention-Guided Graph Convolution Network for Change Detection of Remote Sensing Images

Abstract

Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to capture abundant semantics and complex spatial-temporal correlations of bitemporal features, leading to high uncertainty in distinguishing true changes from pseudo changes. To overcome these limitations, we propose the Attention-guided Graph convolution network for Change Detection (AGCD), a novel framework that integrates a graph convolutional network (GCN) and an attention mechanism to enhance change-detection performance. AGCD introduces three novel modules, including Graph-level Feature Difference Module (GFDM) for enhanced feature interaction, Multi-scale Feature Fusion Module (MFFM) for detailed semantic representation and Spatial-Temporal Attention Module (STAM) for refined spatial-temporal dependency modeling. These modules enable AGCD to reduce pseudo changes triggered by seasonal variations and varying imaging conditions, thereby improving the accuracy and reliability of change-detection results. Extensive experiments on three benchmark datasets demonstrate that AGCD’s superior performance, achieving the best F1-score of 90.34% and IoU of 82.38% on the LEVIR-CD dataset and outperforming existing state-of-the-art methods by a notable margin.

Keywords

• change detection
• graph neural network
• remote sensing image
• attention guided