STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection
Existing change detection techniques exhibit significant deficiencies in the recognition of building edges and detailed textures, making it challenging to accurately distinguish building boundaries from the background. Consequently, these methods struggle to precisely capture complex building contou...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10924762/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Existing change detection techniques exhibit significant deficiencies in the recognition of building edges and detailed textures, making it challenging to accurately distinguish building boundaries from the background. Consequently, these methods struggle to precisely capture complex building contours and subtle texture variations. To address this problem, a spatio-temporal wavelet attention aggregation network (STWANet) is proposed in this article. This network uses a pretrained Resnet18 to extract multiscale features to obtain features with sufficient spatial details and semantic information. We introduce the spatio-temporal differential self-attention module to extract the spatio-temporal difference information between two multiscale temporal features, and the introduction of the self-Attention mechanism is able to focus on the regions with the most significant changes in the multiscale feature maps. In order to extract the changes of detailed features such as building edges, we introduce the wavelet feature enhancement module (WFEM) to enhance the representation of the frequency domain feature information of the changing features, especially the enhancement of high-frequency detail information (e.g., building edges). In order to make up for the shortcomings of WFEM in capturing specific details and global spatial features, we also introduce the dual attention aggregation module to extract the feature information of the changing areas in parallel with WFEM, which can process the spatial context information in a more detailed way, and can better retain the detailed features, especially the complex spatial structure and shape information. spatial structure and shape information. We verify the effectiveness and advancement of STWANet on three classical datasets (LEVIR-CD, WHU-CD, GZ-CD), and the experimental results show that STWANet reaches the state-of-the-art performance level. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |