Problem
Research questions and friction points this paper is trying to address.
multi-source remote sensing
image classification
semantic contextual modeling
feature fusion
spatial misalignment
Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification
🤖 AI Summary
为解决多源遥感图像分类中语义上下文建模不足及特征融合不可靠问题，提出了一种基于语义引导的融合网络SGFNet。
📝 Abstract
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 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. The codes are publicly available at https://github.com/oucailab/SGFNet .
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semantic-Guided Fusion Network
Semantic Mixing Convolution Block
Frequency Modulated Fusion Block
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