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...
Saved in:
| Main Authors: | Zhongqiang Zhang, Ye Wang, Dahua Gao, Haoyong Li, Guangming Shi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520480 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification
by: Ming Sun, et al.
Published: (2024-10-01) -
Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification
by: Yi Liu, et al.
Published: (2025-01-01) -
Detection and Defense: Student-Teacher Network for Adversarial Robustness
by: Kyoungchan Park, et al.
Published: (2024-01-01) -
A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification
by: Aamir Ali, et al.
Published: (2024-05-01) -
DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba
by: Kewen Qu, et al.
Published: (2025-07-01)