Multi-scale capsule Swin Transformer-based method for SAR image target recognition
A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR image target recognition. The network was int...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025045 |
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| _version_ | 1850154319550087168 |
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| author | HOU Yuchao WANG Jie LI Hongtao HAO Yan DUAN Xiaoqi HUANG Kaiwen TIAN Youliang |
| author_facet | HOU Yuchao WANG Jie LI Hongtao HAO Yan DUAN Xiaoqi HUANG Kaiwen TIAN Youliang |
| author_sort | HOU Yuchao |
| collection | DOAJ |
| description | A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR image target recognition. The network was integrated with three parallel capsule Swin Transformer encoding structures, which were fused to classify the input image. Each structure was constructed through a capsule token encoder based on expanded convolutional slice partition and a 3D capsule Swin Transformer module, which designed to capture of more profound and extensive semantic features.The experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset and FUSAR-Ship dataset were shown to demonstrate that MSCSTN outperformed other methods under various test conditions. The results demonstrate that MSCSTN exhibits excellent recognition performance, generalization ability, and potential for application. |
| format | Article |
| id | doaj-art-e66dcda574b2475d84f83d61cb08edd0 |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-e66dcda574b2475d84f83d61cb08edd02025-08-20T02:25:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-03-014627429088698488Multi-scale capsule Swin Transformer-based method for SAR image target recognitionHOU YuchaoWANG JieLI HongtaoHAO YanDUAN XiaoqiHUANG KaiwenTIAN YouliangA multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR image target recognition. The network was integrated with three parallel capsule Swin Transformer encoding structures, which were fused to classify the input image. Each structure was constructed through a capsule token encoder based on expanded convolutional slice partition and a 3D capsule Swin Transformer module, which designed to capture of more profound and extensive semantic features.The experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset and FUSAR-Ship dataset were shown to demonstrate that MSCSTN outperformed other methods under various test conditions. The results demonstrate that MSCSTN exhibits excellent recognition performance, generalization ability, and potential for application.http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025045dilated convolution patch partitioncapsule token encoderthree-dimensional capsule Swin Transformer modulemulti scale capsule Swin Transformer networkSAR image target recognition |
| spellingShingle | HOU Yuchao WANG Jie LI Hongtao HAO Yan DUAN Xiaoqi HUANG Kaiwen TIAN Youliang Multi-scale capsule Swin Transformer-based method for SAR image target recognition Tongxin xuebao dilated convolution patch partition capsule token encoder three-dimensional capsule Swin Transformer module multi scale capsule Swin Transformer network SAR image target recognition |
| title | Multi-scale capsule Swin Transformer-based method for SAR image target recognition |
| title_full | Multi-scale capsule Swin Transformer-based method for SAR image target recognition |
| title_fullStr | Multi-scale capsule Swin Transformer-based method for SAR image target recognition |
| title_full_unstemmed | Multi-scale capsule Swin Transformer-based method for SAR image target recognition |
| title_short | Multi-scale capsule Swin Transformer-based method for SAR image target recognition |
| title_sort | multi scale capsule swin transformer based method for sar image target recognition |
| topic | dilated convolution patch partition capsule token encoder three-dimensional capsule Swin Transformer module multi scale capsule Swin Transformer network SAR image target recognition |
| url | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025045 |
| work_keys_str_mv | AT houyuchao multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition AT wangjie multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition AT lihongtao multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition AT haoyan multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition AT duanxiaoqi multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition AT huangkaiwen multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition AT tianyouliang multiscalecapsuleswintransformerbasedmethodforsarimagetargetrecognition |