A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling
Remote-sensing image-change detection is indispensable for land management, environmental monitoring and related applications. In recent years, breakthroughs in satellite sensor technology have generated vast volumes of data and complex scenes, presenting significant challenges for change-detection...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2226 |
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| author | Enze Zhang Yan Li Haifeng Lin Min Xia |
| author_facet | Enze Zhang Yan Li Haifeng Lin Min Xia |
| author_sort | Enze Zhang |
| collection | DOAJ |
| description | Remote-sensing image-change detection is indispensable for land management, environmental monitoring and related applications. In recent years, breakthroughs in satellite sensor technology have generated vast volumes of data and complex scenes, presenting significant challenges for change-detection algorithms. Traditional methods rely on handcrafted features, which struggle to address the impacts of multi-source data heterogeneity and imaging condition differences. In this context, technology based on deep learning has made substantial breakthroughs in change-detection performance by automatically extracting high-level feature representations of the data. However, although the existing deep-learning models improve the detection accuracy through end-to-end learning, their high parameter count and computational inefficiency hinder suitability for real-time monitoring and edge device deployment. Therefore, to address the need for lightweight solutions in scenarios with limited computing resources, this paper proposes an attention-based lightweight remote sensing change detection network (ABLRCNet), which achieves a balance between computational efficiency and detection accuracy by using lightweight residual convolution blocks (LRCBs), multi-scale spatial-attention modules (MSAMs) and feature-difference enhancement modules (FDEMs). The experimental results demonstrate that the ABLRCNet achieves excellent performance on three datasets, significantly enhancing both the accuracy and robustness of change detection, while exhibiting efficient detection capabilities in resource-limited scenarios. |
| format | Article |
| id | doaj-art-e15ae1a04839405d9e5e9772c1ba58eb |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e15ae1a04839405d9e5e9772c1ba58eb2025-08-20T03:28:59ZengMDPI AGRemote Sensing2072-42922025-06-011713222610.3390/rs17132226A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention CouplingEnze Zhang0Yan Li1Haifeng Lin2Min Xia3Reading Academy, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaReading Academy, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaReading Academy, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaRemote-sensing image-change detection is indispensable for land management, environmental monitoring and related applications. In recent years, breakthroughs in satellite sensor technology have generated vast volumes of data and complex scenes, presenting significant challenges for change-detection algorithms. Traditional methods rely on handcrafted features, which struggle to address the impacts of multi-source data heterogeneity and imaging condition differences. In this context, technology based on deep learning has made substantial breakthroughs in change-detection performance by automatically extracting high-level feature representations of the data. However, although the existing deep-learning models improve the detection accuracy through end-to-end learning, their high parameter count and computational inefficiency hinder suitability for real-time monitoring and edge device deployment. Therefore, to address the need for lightweight solutions in scenarios with limited computing resources, this paper proposes an attention-based lightweight remote sensing change detection network (ABLRCNet), which achieves a balance between computational efficiency and detection accuracy by using lightweight residual convolution blocks (LRCBs), multi-scale spatial-attention modules (MSAMs) and feature-difference enhancement modules (FDEMs). The experimental results demonstrate that the ABLRCNet achieves excellent performance on three datasets, significantly enhancing both the accuracy and robustness of change detection, while exhibiting efficient detection capabilities in resource-limited scenarios.https://www.mdpi.com/2072-4292/17/13/2226change detectionattention mechanismmulti-scale fusionlightweight networkdeep learning |
| spellingShingle | Enze Zhang Yan Li Haifeng Lin Min Xia A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling Remote Sensing change detection attention mechanism multi-scale fusion lightweight network deep learning |
| title | A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling |
| title_full | A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling |
| title_fullStr | A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling |
| title_full_unstemmed | A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling |
| title_short | A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling |
| title_sort | lightweight remote sensing image change detection algorithm based on asymmetric convolution and attention coupling |
| topic | change detection attention mechanism multi-scale fusion lightweight network deep learning |
| url | https://www.mdpi.com/2072-4292/17/13/2226 |
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