Change Detection Meets Frequency Learning: A Coarse-to-Fine Dual-Domain Detection Network

Change detection (CD) aims to distinguish the changed regions in bitemporal images under the same area. Accurate detection needs comprehensive and precise semantic information extraction. Naturally, multiscale feature extraction becomes a fashion. Recent methods implement multiscale feature extraction through complicated convolutional neural network (CNN) modules or Transformers. However, complicated modules are far from the practical demands. First, implementing multiscale feature extraction through multilevel stacking of modules leads to unnecessary computing. Not all regions in images need very detailed detection. Second, respective modules for different scale extractions make discrete resolutions on detection. We need a more continuous recognition of spatial resolutions. Third, inherent weakness in high-frequency learning of CNNs and Transformers degrades the detection of details. In this article, we develop a new multiscale architecture, cross-domain coarse-to-fine (CDC2F) network. Specifically, we first perform coarse detection in the spatial domain. Based on the coarse change map (CM), the bitemporal images will be segmented into blocks, which are then go through a filtering process to retain the mixed blocks. For the retained blocks, a fine detection will be conducted in the frequency domain. Finally, spatial-frequency features are fused to make the final detection. The proposed coarse-to-fine (C2F) strategy guarantees computational efficiency. The cross-domain architecture (spatial and frequency) provides a continuous scale recognition through discrete cosine transform (DCT). And the explicit frequency learning makes detailed detection come true. Extensive experiments on three widely used CD datasets [learning, vision, and remote sensing CD (LEVIR-CD), Wuhan University (WHU), and season-varying CD (SVCD)] show that CDC2F achieves state-of-the-art (SOTA) performance in both the evaluation metrics and the visual presentation, with few parameters and low computational complexity. The source code is available at https://github.com/Beat992/CDC2F.