Building Change Detection Using Cross-Temporal Feature Interaction Network

Building change detection of remote sensing images is in full flourishing accompanied by the prosperity of convolutional neural networks. For spatial-temporal context modeling, existing solutions disregard the inter-image interactions, albeit their positive contribution to the acquisition of differences. To fill the gap, we propose a cross-temporal feature interaction network to effectively derive the change representations. Specifically, we propose a linearized cross-attention, which motivates each counterpart to glimpse the representation of another image while preserving its own features. In addition, to circumvent the misalignment caused by step-down sampling in the backbone, we introduce multi-level feature alignment using learnable affine transformation and stepwise aggregation. Based on a naive backbone (ResNet18) without sophisticated structures, our model outperforms other state-of-the-art methods on three datasets in terms of both efficiency and effectiveness.