🚀 Semantic-Guided Fusion Network for Multi-source Remote Sensing Image Classification, submitted to IEEE GRSL
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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, thereby enhancing semantic dependency modeling and adaptive feature aggregation. In addition, the Frequency Modulated Fusion Block (FMFB) is introduced to perform cross-modal interaction in the frequency domain, which effectively alleviates the influence of slight spatial misalignment and improves complementary information fusion. Extensive experiments conducted on the Augsburg and Houston 2018 datasets demonstrate that the proposed SGFNet consistently outperforms several state-of-the-art methods.
If you have any other questions, feel free to contact me at gaofeng@ouc.edu.cn .