Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification
Multi-source remote sensing image classification has attracted increasing attention due to the complementary spectral, structural, and geometric information. However, existing methods still suffer from two limitations: insufficient semantic contextual modeling and unreliable feature fusion caused by slight spatial misalignment. To address these issues, we propose a Semantic-Guided Fusion Network (SGFNet) for multi-source remote sensing image classification. Specifically, the Semantic Mixing Convolution Block (SMCB) is designed to dynamically generate semantic-aware convolution kernels according to contextual relationships among feature representations. In addition, the Frequency Modulated...
| Authors | Yuwei Zhao, Chuanzheng Gong, Baogui Huan et al. |
|---|---|
| Primary category | eess.IV |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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