New MSCM-net model combines CNNs and Mamba for hyperspectral image classification

Researchers have developed MSCM-net, a novel hyperspectral image classification model that combines multi-scale convolutional neural networks (CNNs) with Mamba blocks. This architecture aims to improve classification performance and reduce computational complexity by integrating the local feature extraction of CNNs with the long-range modeling capabilities of Mamba. The model incorporates a multi-scale feature extraction module with SENet and a dual-branch feature aggregation module to enhance spatial and spectral information integration. Experiments on benchmark datasets indicate that MSCM-net achieves advanced classification results. AI

IMPACT Introduces a novel architecture for hyperspectral image classification, potentially improving accuracy and efficiency in remote sensing and engineering applications.

RANK_REASON The item describes a new model architecture presented in an academic paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• CNN
• Mamba
• MSCM-net
• Transformer++

• paper
• model release

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