Triple Change Detection Network via Joint Multifrequency and Full-Scale Swin-Transformer for Remote Sensing Images

Although deep learning-based change detection (CD) methods achieve great success in remote sensing images, they still suffer from two main challenges. First, popular convolutional neural networks (CNNs) are weak in extracting discriminated features focusing on changed regions, since most methods ignore the multifrequency components of bitemporal images. Second, although existing CD methods employ the Transformer structure to capture long-range dependency for global feature representation, it is difficult for them to simultaneously take into account the long-range dependency of changed objects at various scales. To address the above issues, we propose a triple CD network (TCD-Net) via joint multifrequency and full-scale swin-transformer (FST). The proposed TCD-Net has two main advantages. First, we propose a multifrequency channel attention (MFCA) module to boost the ability of modeling the channel correlation, which can compensate for the problem of insufficient feature representation caused by only performing global average pooling (GAP). Furthermore, a joint multifrequency difference feature enhancement (JM-DFE) guiding block is proposed to improve the boundary quality and the position awareness of truly changed objects, which can effectively extract channel features of multifrequency information and thus improve the discriminative ability of features. Second, unlike Siamese-based structures, we propose an FST module as the third branch to model and aggregate the long-range dependency of multiscale changed objects, which can alleviate the missed detections of small objects and achieve more compact changed regions effectively. Experiments on three public CD datasets exhibit that the proposed TCD-Net achieves better CD accuracy with smaller model complexity than state-of-the-art (SOTA) methods. The code is publicly available at
https://github.com/RSCD-mz/TCD-Net
.