PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT)
This paper Introduces a novel method for Polarimetric Synthetic Aperture Radar (PolSAR) image classification using a Complex-Valued Multiscale Attention Vision Transformer (CV-MsAtViT). The model incorporates a complex-valued multiscale feature fusion mechanism, a complex-valued attention block, and...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000597 |
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| _version_ | 1850252570022379520 |
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| author | Mohammed Q. Alkhatib |
| author_facet | Mohammed Q. Alkhatib |
| author_sort | Mohammed Q. Alkhatib |
| collection | DOAJ |
| description | This paper Introduces a novel method for Polarimetric Synthetic Aperture Radar (PolSAR) image classification using a Complex-Valued Multiscale Attention Vision Transformer (CV-MsAtViT). The model incorporates a complex-valued multiscale feature fusion mechanism, a complex-valued attention block, and a Complex-Valued Vision Transformer (CV-ViT) to effectively capture spatial and polarimetric features from PolSAR data. The multiscale fusion block enhances feature extraction, while the attention mechanism prioritizes critical features, and the CV-ViT processes data in the complex domain, preserving both amplitude and phase information. Experimental results on benchmark PolSAR datasets, including Flevoland, San Francisco, and Oberpfaffenhofen, show that CV-MsAtViT achieves superior classification accuracy, with an overall accuracy (OA) of 98.35% on the Flevoland dataset, outperforming state-of-the-art models like PolSARFormer. The model also demonstrates efficient computational performance, minimizing the number of parameters while preserving high accuracy. These results confirm that CV-MsAtViT effectively enhances the classification of PolSAR images by leveraging complex-valued data processing, offering a promising direction for future advancements in remote sensing and complex-valued deep learning.The codes associated with this paper are publicly available at https://github.com/mqalkhatib/CV-MsAtViT. |
| format | Article |
| id | doaj-art-9c5deb3d95724568bf9a0ab5fecce0f6 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-9c5deb3d95724568bf9a0ab5fecce0f62025-08-20T01:57:36ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-03-0113710441210.1016/j.jag.2025.104412PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT)Mohammed Q. Alkhatib0College of Engineering and IT, University of Dubai, Dubai, 14143, United Arab EmiratesThis paper Introduces a novel method for Polarimetric Synthetic Aperture Radar (PolSAR) image classification using a Complex-Valued Multiscale Attention Vision Transformer (CV-MsAtViT). The model incorporates a complex-valued multiscale feature fusion mechanism, a complex-valued attention block, and a Complex-Valued Vision Transformer (CV-ViT) to effectively capture spatial and polarimetric features from PolSAR data. The multiscale fusion block enhances feature extraction, while the attention mechanism prioritizes critical features, and the CV-ViT processes data in the complex domain, preserving both amplitude and phase information. Experimental results on benchmark PolSAR datasets, including Flevoland, San Francisco, and Oberpfaffenhofen, show that CV-MsAtViT achieves superior classification accuracy, with an overall accuracy (OA) of 98.35% on the Flevoland dataset, outperforming state-of-the-art models like PolSARFormer. The model also demonstrates efficient computational performance, minimizing the number of parameters while preserving high accuracy. These results confirm that CV-MsAtViT effectively enhances the classification of PolSAR images by leveraging complex-valued data processing, offering a promising direction for future advancements in remote sensing and complex-valued deep learning.The codes associated with this paper are publicly available at https://github.com/mqalkhatib/CV-MsAtViT.http://www.sciencedirect.com/science/article/pii/S1569843225000597PolSAR image classificationComplex-valued (CV) networksFeature fusionCV coordinate attentionCV vision transformers (CV-viT) |
| spellingShingle | Mohammed Q. Alkhatib PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT) International Journal of Applied Earth Observations and Geoinformation PolSAR image classification Complex-valued (CV) networks Feature fusion CV coordinate attention CV vision transformers (CV-viT) |
| title | PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT) |
| title_full | PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT) |
| title_fullStr | PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT) |
| title_full_unstemmed | PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT) |
| title_short | PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT) |
| title_sort | polsar image classification using complex valued multiscale attention vision transformer cv msatvit |
| topic | PolSAR image classification Complex-valued (CV) networks Feature fusion CV coordinate attention CV vision transformers (CV-viT) |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000597 |
| work_keys_str_mv | AT mohammedqalkhatib polsarimageclassificationusingcomplexvaluedmultiscaleattentionvisiontransformercvmsatvit |