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Multi-domain adaptive fusion network for multi-source remote sensing data classification
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AI
DOI：10.1007/s11432-025-4969-8.png)
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Abstract
En 中文
The rapid progression of remote sensing technologies has positioned the effective exploitation of multi-source data—e.g., hyperspectral imaging (HSI) and light detection and ranging (LiDAR) data—for land cover classification as a pivotal research domain. However, existing methods fail to fully consider the interactions between different source features. Additionally, how to fully exploit discriminative structural information in the frequency domain remains a critical challenge. To this end, a novel multi-domain adaptive fusion network (MDAF-Net) is proposed for the joint classification of HSI and LiDAR data. MDAF-Net integrates multi-scale feature extraction, adaptive spatial-channel interaction, and frequency-aware fusion, which achieves comprehensive multi-domain representation and fusion across the spatial, spectral, and frequency domains. Specifically, a multi-scale module is first introduced to extract hierarchical features from the input data. Then, to enhance adaptive interaction between spatial and channel dimensions, an adaptive spatial-channel exchange module (ASCEM) is proposed to capture intra-feature dependencies and cross-modal correlations. Finally, a frequency-aware fusion module (FAFM) is proposed to fully leverage frequency domain information. This module adaptively adjusts frequency components, enhancing structural feature representations. Extensive experiments on three widely used datasets demonstrate that the proposed method outperforms existing state-of-the-art methods.
Keywords:
hyperspectral imaging (HSI)
light detection and ranging (LiDAR) data
multi-scale
spatial-channel interaction
frequency-aware fusion
Journal
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IF:
7.6
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86
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