PolicyMamba: Localized Policy Attention With State Space Model for Land Cover Classification

Ahmad, Muhammad; Mazzara, Manuel; Distefano, Salvatore; Mehmood Khan, Adil; Hassaan Farooq Butt, Muhammad; Hong, Danfeng

Abstract

Multihead self-attention and cross-attention mechanisms often suffer from computational inefficiencies, limited scalability, and suboptimal contextual understanding, particularly in hyperspectral image (HSI) classification. These mechanisms struggle to effectively capture long-range dependencies while maintaining computational feasibility due to the quadratic complexity of self-attention. To address these challenges, this work proposes PolicyMamba, a spectral–spatial mamba model enhanced with a localized policy attention mechanism. This mechanism reduces computational overhead by restricting attention to nonoverlapping localized regions and enforcing sparsity constraints, ensuring that only the most informative interactions are retained. A hierarchical aggregation strategy further integrates patch-wise attention outputs, preserving spectral–spatial correlations across scales. In addition, a sliding window patch process enhances local feature continuity while mitigating information loss. The PolicyMamba framework integrates spectral–spatial token generation, token enhancement, localized attention, and state transition modules, significantly improving HSI feature representation. Extensive experiments demonstrate that PolicyMamba achieves superior classification accuracy, outperforming conventional and state-of-the-art methods in land cover classification (LCC) by efficiently modeling intricate dependencies in HSI data.

Citation

Ahmad, M., Mazzara, M., Distefano, S., Mehmood Khan, A., Hassaan Farooq Butt, M., & Hong, D. (in press). PolicyMamba: Localized Policy Attention With State Space Model for Land Cover Classification. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/tnnls.2025.3586836

• Journal Article Type: Article
• Acceptance Date: Jul 3, 2025
• Online Publication Date: Jul 22, 2025
• Deposit Date: Jul 24, 2025
• Publicly Available Date: Jul 25, 2025
• Journal: IEEE Transactions on Neural Networks and Learning Systems
• Print ISSN: 2162-237X
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/tnnls.2025.3586836
• Keywords: Land cover classification (LCC); Sparsity-constrained attention mechanism; Spatial–spectral mamba
• Public URL: https://hull-repository.worktribe.com/output/5290572