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Multimodal semantic-scale network for remote sensing image classification
DOI:10.1016/j.inffus.2026.104361.png)
摘要
En 中文
• 结合基于欧几里得语义自注意力机制的卷积。
• 可适应尺度模块,平衡降维与特征恢复。
• 轻量化高性能架构适用于实际应用。
Keyword:
convolution
semantic self-attention
scale-adaptive module
lightweight architecture
remote sensing image classification
期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
机构
引用论文
Cross-source transformer-based neighborhood contrastive learning for joint classification of hyperspectral and LiDAR Data基于跨源Transformer的邻域对比学习,用于高光谱和LiDAR数据的联合分类
Information Fusion
IF15.5
Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification面向高光谱图像分类的层次卷积特征研究
Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification基于3D卷积自动编码器的无监督空间光谱特征学习,用于高光谱分类

