Spectral–spatial mamba adversarial defense network for hyperspectral image classification
Deep learning models have obtained great success in hyperspectral image classification tasks. Nevertheless, they are usually vulnerable to adversarial attacks. Some existing works have been made to defend against adversarial attacks in HSI classification. These works primarily focus on lots of adver...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520480 |
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| author | Zhongqiang Zhang Ye Wang Dahua Gao Haoyong Li Guangming Shi |
| author_facet | Zhongqiang Zhang Ye Wang Dahua Gao Haoyong Li Guangming Shi |
| author_sort | Zhongqiang Zhang |
| collection | DOAJ |
| description | Deep learning models have obtained great success in hyperspectral image classification tasks. Nevertheless, they are usually vulnerable to adversarial attacks. Some existing works have been made to defend against adversarial attacks in HSI classification. These works primarily focus on lots of adversarial samples and spatial relationships while overlooking the strong long-range dependencies from HSI. To alleviate this problem, we propose a novel spectral spatial mamba adversarial defense network (SSMADNet) for hyperspectral adversarial image classification. It includes a dense involution branch, a spectral mamba branch, and a spatial multiscale mamba branch. The dense involution branch extracts embedding features via three dense involution layers. The spectral mamba branch can learn the spectral sequence information from HSI adversarial samples. The spatial multiscale mamba branch can model the long-range interaction of the whole image. Finally, a spectral spatial feature enhancement module is designed to adaptively enhance useful spectral spatial features of HSI. Extensive experimental results demonstrate that on five HSI adversarial datasets, the proposed SSMADNet achieves higher classification accuracies than state-of-the-art adversarial defense methods. In particular, our method obtains best OA (93.80%) on the Botswana adversarial data, which is much higher than the suboptimal method (OA = 90.30%). |
| format | Article |
| id | doaj-art-9d3f02233a334e059bf5626305fbba9b |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-9d3f02233a334e059bf5626305fbba9b2025-08-25T11:25:07ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2520480Spectral–spatial mamba adversarial defense network for hyperspectral image classificationZhongqiang Zhang0Ye Wang1Dahua Gao2Haoyong Li3Guangming Shi4Peng Cheng Laboratory, Shenzhen, People’s Republic of ChinaPeng Cheng Laboratory, Shenzhen, People’s Republic of ChinaSchool of Artificial Intelligence, Xidian University, Xi’an, People’s Republic of ChinaSchool of Artificial Intelligence, Xidian University, Xi’an, People’s Republic of ChinaPeng Cheng Laboratory, Shenzhen, People’s Republic of ChinaDeep learning models have obtained great success in hyperspectral image classification tasks. Nevertheless, they are usually vulnerable to adversarial attacks. Some existing works have been made to defend against adversarial attacks in HSI classification. These works primarily focus on lots of adversarial samples and spatial relationships while overlooking the strong long-range dependencies from HSI. To alleviate this problem, we propose a novel spectral spatial mamba adversarial defense network (SSMADNet) for hyperspectral adversarial image classification. It includes a dense involution branch, a spectral mamba branch, and a spatial multiscale mamba branch. The dense involution branch extracts embedding features via three dense involution layers. The spectral mamba branch can learn the spectral sequence information from HSI adversarial samples. The spatial multiscale mamba branch can model the long-range interaction of the whole image. Finally, a spectral spatial feature enhancement module is designed to adaptively enhance useful spectral spatial features of HSI. Extensive experimental results demonstrate that on five HSI adversarial datasets, the proposed SSMADNet achieves higher classification accuracies than state-of-the-art adversarial defense methods. In particular, our method obtains best OA (93.80%) on the Botswana adversarial data, which is much higher than the suboptimal method (OA = 90.30%).https://www.tandfonline.com/doi/10.1080/17538947.2025.2520480Adversarial attacksspectral spatial mamba adversarial defense networkdense involution branchspatial multiscale mamba branch |
| spellingShingle | Zhongqiang Zhang Ye Wang Dahua Gao Haoyong Li Guangming Shi Spectral–spatial mamba adversarial defense network for hyperspectral image classification International Journal of Digital Earth Adversarial attacks spectral spatial mamba adversarial defense network dense involution branch spatial multiscale mamba branch |
| title | Spectral–spatial mamba adversarial defense network for hyperspectral image classification |
| title_full | Spectral–spatial mamba adversarial defense network for hyperspectral image classification |
| title_fullStr | Spectral–spatial mamba adversarial defense network for hyperspectral image classification |
| title_full_unstemmed | Spectral–spatial mamba adversarial defense network for hyperspectral image classification |
| title_short | Spectral–spatial mamba adversarial defense network for hyperspectral image classification |
| title_sort | spectral spatial mamba adversarial defense network for hyperspectral image classification |
| topic | Adversarial attacks spectral spatial mamba adversarial defense network dense involution branch spatial multiscale mamba branch |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520480 |
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