An attention-based multiscale transformer network for remote sensing image change detection
The bi-temporal change detection (CD) is still challenging for high-resolution optical remote sensing data analysis due to various factors such as complex textures, seasonal variations, climate changes, and new requirements. We propose an attention-based multiscale transformer network (AMTNet) that...
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| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 202; pp. 599 - 609 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.08.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The bi-temporal change detection (CD) is still challenging for high-resolution optical remote sensing data analysis due to various factors such as complex textures, seasonal variations, climate changes, and new requirements. We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer architecture uses ConvNets as the backbone to extract multiscale features from the raw input image pair. We then employ attention and transformer modules to model contextual information in bi-temporal images effectively. Additionally, we use feature exchange to bridge the domain gap between different temporal image domains by partially exchanging features between the two Siamese branches of our AMTNet. Experimental results on four commonly used CD datasets – CLCD, HRSCD, WHU-CD, and LEVIR-CD – demonstrate the effectiveness and efficiency of our proposed AMTNet approach. The code for this work will be available on GitHub.11https://github.com/linyiyuan11/AMT_Net. |
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| AbstractList | The bi-temporal change detection (CD) is still challenging for high-resolution optical remote sensing data analysis due to various factors such as complex textures, seasonal variations, climate changes, and new requirements. We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer architecture uses ConvNets as the backbone to extract multiscale features from the raw input image pair. We then employ attention and transformer modules to model contextual information in bi-temporal images effectively. Additionally, we use feature exchange to bridge the domain gap between different temporal image domains by partially exchanging features between the two Siamese branches of our AMTNet. Experimental results on four commonly used CD datasets – CLCD, HRSCD, WHU-CD, and LEVIR-CD – demonstrate the effectiveness and efficiency of our proposed AMTNet approach. The code for this work will be available on GitHub.11https://github.com/linyiyuan11/AMT_Net. The bi-temporal change detection (CD) is still challenging for high-resolution optical remote sensing data analysis due to various factors such as complex textures, seasonal variations, climate changes, and new requirements. We propose an attention-based multiscale transformer network (AMTNet) that utilizes a CNN-transformer structure to address this issue. Our Siamese network based on the CNN-transformer architecture uses ConvNets as the backbone to extract multiscale features from the raw input image pair. We then employ attention and transformer modules to model contextual information in bi-temporal images effectively. Additionally, we use feature exchange to bridge the domain gap between different temporal image domains by partially exchanging features between the two Siamese branches of our AMTNet. Experimental results on four commonly used CD datasets – CLCD, HRSCD, WHU-CD, and LEVIR-CD – demonstrate the effectiveness and efficiency of our proposed AMTNet approach. The code for this work will be available on GitHub.¹1https://github.com/linyiyuan11/AMT_Net. |
| Author | Lin, Yiyuan Liu, Wei Yu, Yongtao Li, Jonathan Liu, Weijia |
| Author_xml | – sequence: 1 givenname: Wei orcidid: 0000-0001-5463-9991 surname: Liu fullname: Liu, Wei organization: School of Software, East China Jiaotong University, Nanchang 330013, China – sequence: 2 givenname: Yiyuan surname: Lin fullname: Lin, Yiyuan organization: School of Software, East China Jiaotong University, Nanchang 330013, China – sequence: 3 givenname: Weijia surname: Liu fullname: Liu, Weijia organization: School of Software, East China Jiaotong University, Nanchang 330013, China – sequence: 4 givenname: Yongtao orcidid: 0000-0001-7204-9346 surname: Yu fullname: Yu, Yongtao email: allennessy@hyit.edu.cn organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 5 givenname: Jonathan surname: Li fullname: Li, Jonathan organization: Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada |
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