Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915189633318912 |
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| author | Lin, Junyan Gap, Feng Qi, Lin Dong, Junyu Du, Qian Gao, Xinbo |
| author_facet | Lin, Junyan Gap, Feng Qi, Lin Dong, Junyu Du, Qian Gao, Xinbo |
| contents | Hyperspectral image (HSI) and LiDAR data joint classification is a challenging task. Existing multi-source remote sensing data classification methods often rely on human-designed frameworks for feature extraction, which heavily depend on expert knowledge. To address these limitations, we propose a novel Dynamic Cross-Modal Feature Interaction Network (DCMNet), the first framework leveraging a dynamic routing mechanism for HSI and LiDAR classification. Specifically, our approach introduces three feature interaction blocks: Bilinear Spatial Attention Block (BSAB), Bilinear Channel Attention Block (BCAB), and Integration Convolutional Block (ICB). These blocks are designed to effectively enhance spatial, spectral, and discriminative feature interactions. A multi-layer routing space with routing gates is designed to determine optimal computational paths, enabling data-dependent feature fusion. Additionally, bilinear attention mechanisms are employed to enhance feature interactions in spatial and channel representations. Extensive experiments on three public HSI and LiDAR datasets demonstrate the superiority of DCMNet over state-of-the-art methods. Our code will be available at https://github.com/oucailab/DCMNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification Lin, Junyan Gap, Feng Qi, Lin Dong, Junyu Du, Qian Gao, Xinbo Image and Video Processing Computer Vision and Pattern Recognition Hyperspectral image (HSI) and LiDAR data joint classification is a challenging task. Existing multi-source remote sensing data classification methods often rely on human-designed frameworks for feature extraction, which heavily depend on expert knowledge. To address these limitations, we propose a novel Dynamic Cross-Modal Feature Interaction Network (DCMNet), the first framework leveraging a dynamic routing mechanism for HSI and LiDAR classification. Specifically, our approach introduces three feature interaction blocks: Bilinear Spatial Attention Block (BSAB), Bilinear Channel Attention Block (BCAB), and Integration Convolutional Block (ICB). These blocks are designed to effectively enhance spatial, spectral, and discriminative feature interactions. A multi-layer routing space with routing gates is designed to determine optimal computational paths, enabling data-dependent feature fusion. Additionally, bilinear attention mechanisms are employed to enhance feature interactions in spatial and channel representations. Extensive experiments on three public HSI and LiDAR datasets demonstrate the superiority of DCMNet over state-of-the-art methods. Our code will be available at https://github.com/oucailab/DCMNet. |
| title | Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.06945 |