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...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10924762/ |
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| author | Xiaoyang Zhang Kaihui Dong Dapeng Cheng Zhen Hua Jinjiang Li |
| author_facet | Xiaoyang Zhang Kaihui Dong Dapeng Cheng Zhen Hua Jinjiang Li |
| author_sort | Xiaoyang Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aebaff624e9b416a8a6c9849d637f672 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-aebaff624e9b416a8a6c9849d637f6722025-08-20T03:05:49ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01188813883010.1109/JSTARS.2025.355109310924762STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change DetectionXiaoyang Zhang0Kaihui Dong1Dapeng Cheng2https://orcid.org/0009-0003-4408-5114Zhen Hua3https://orcid.org/0000-0003-1638-2974Jinjiang Li4https://orcid.org/0000-0002-2080-8678School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaExisting 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.https://ieeexplore.ieee.org/document/10924762/Attention mechanismchange detectionfrequency domain featuresremote sensingwavelet transform |
| spellingShingle | Xiaoyang Zhang Kaihui Dong Dapeng Cheng Zhen Hua Jinjiang Li STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention mechanism change detection frequency domain features remote sensing wavelet transform |
| title | STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection |
| title_full | STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection |
| title_fullStr | STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection |
| title_full_unstemmed | STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection |
| title_short | STWANet: Spatio-Temporal Wavelet Attention Aggregation Network for Remote Sensing Change Detection |
| title_sort | stwanet spatio temporal wavelet attention aggregation network for remote sensing change detection |
| topic | Attention mechanism change detection frequency domain features remote sensing wavelet transform |
| url | https://ieeexplore.ieee.org/document/10924762/ |
| work_keys_str_mv | AT xiaoyangzhang stwanetspatiotemporalwaveletattentionaggregationnetworkforremotesensingchangedetection AT kaihuidong stwanetspatiotemporalwaveletattentionaggregationnetworkforremotesensingchangedetection AT dapengcheng stwanetspatiotemporalwaveletattentionaggregationnetworkforremotesensingchangedetection AT zhenhua stwanetspatiotemporalwaveletattentionaggregationnetworkforremotesensingchangedetection AT jinjiangli stwanetspatiotemporalwaveletattentionaggregationnetworkforremotesensingchangedetection |