语义引导融合网络用于多源遥感图像分类
Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification
- Ocean University of China（中国海洋大学）
- Mississippi State University（密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对多源遥感图像分类中语义建模不足和空间错位导致的融合不可靠问题，提出SGFNet，通过SMCB动态生成语义卷积核和FMFB频域交互，在Augsburg和Houston 2018数据集上优于现有方法。
AI中文摘要：
多源遥感图像分类因其互补的光谱、结构和几何信息而受到越来越多的关注。然而，现有方法仍存在两个局限性：语义上下文建模不足，以及由轻微空间错位导致的不可靠特征融合。为解决这些问题，我们提出了一种用于多源遥感图像分类的语义引导融合网络（SGFNet）。具体而言，设计了语义混合卷积块（SMCB），根据特征表示之间的上下文关系动态生成语义感知的卷积核。此外，引入了频率调制融合块（FMFB），在频域中进行跨模态交互，有效减轻了轻微空间错位的影响，并改善了互补信息的融合。在奥格斯堡和休斯顿2018数据集上进行的大量实验表明，所提出的SGFNet持续优于多种最先进的方法。代码已在https://github.com/oucailab/SGFNet 公开提供。
英文摘要：
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 .
