PolicyMamba: Localized Policy Attention With State Space Model for Land Cover Classification
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View abstract on PubMed
Summary
This summary is machine-generated.PolicyMamba enhances hyperspectral image (HSI) classification by using a novel spectral-spatial mamba model with localized policy attention. This approach improves computational efficiency and accuracy in land cover classification tasks.
Area Of Science
- Computer Vision
- Remote Sensing
- Machine Learning
Background
- Multihead attention mechanisms in hyperspectral image (HSI) classification face computational inefficiencies and scalability issues.
- Capturing long-range dependencies in HSI data is challenging due to the quadratic complexity of self-attention.
Purpose Of The Study
- To introduce PolicyMamba, an efficient spectral-spatial mamba model for improved HSI classification.
- To address the limitations of traditional attention mechanisms in HSI analysis.
Main Methods
- PolicyMamba utilizes a localized policy attention mechanism to reduce computational overhead by focusing on non-overlapping regions and enforcing sparsity.
- A hierarchical aggregation strategy integrates patch-wise attention outputs to preserve spectral-spatial correlations across scales.
- A sliding window patch process is employed to enhance local feature continuity and minimize information loss.
Main Results
- PolicyMamba demonstrates superior classification accuracy in land cover classification (LCC) compared to conventional and state-of-the-art methods.
- The model efficiently models intricate spectral-spatial dependencies within HSI data.
- Experimental results validate the effectiveness of the proposed localized policy attention and hierarchical aggregation.
Conclusions
- PolicyMamba offers a computationally efficient and scalable solution for HSI classification.
- The proposed model significantly enhances feature representation and classification performance.
- This work provides a new direction for developing advanced deep learning models for HSI analysis.
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