动态融合高光谱与激光雷达数据，提升遥感分类精度
Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification
- 用动态路由机制自动选择最优特征融合路径
- 在三个公开数据集上准确率超现有方法
- 适合需要多源遥感数据融合的科研与应用
高光谱图像（HSI）与激光雷达（LiDAR）数据联合分类是一项挑战性任务。现有方法多依赖人工设计的特征提取框架，严重依赖专家知识。为此，我们提出首个利用动态路由机制的高光谱与激光雷达分类框架——动态跨模态特征交互网络（DCMNet）。该方法包含三个特征交互模块：双线性空间注意力块（BSAB）、双线性通道注意力块（BCAB）和集成卷积块（ICB），有效增强空间、光谱及判别性特征交互。设计多层路由空间与路由门，根据数据自适应确定最优计算路径，实现数据驱动的特征融合。同时，采用双线性注意力机制强化空间与通道维度的特征交互。在三个公开的高光谱与激光雷达数据集上进行的大量实验表明，DCMNet显著优于当前最先进方法。代码将开源于 https://github.com/oucailab/DCMNet。
原文摘要 · Abstract (English)
Hyperspectral image (HSI) and LiDAR data joint classification is a challenging task. Existing multi-source remote sensing data classification methods often rely on human-designed frameworks for feature extraction, which heavily depend on expert knowledge. To address these limitations, we propose a novel Dynamic Cross-Modal Feature Interaction Network (DCMNet), the first framework leveraging a dynamic routing mechanism for HSI and LiDAR classification. Specifically, our approach introduces three feature interaction blocks: Bilinear Spatial Attention Block (BSAB), Bilinear Channel Attention Block (BCAB), and Integration Convolutional Block (ICB). These blocks are designed to effectively enhance spatial, spectral, and discriminative feature interactions. A multi-layer routing space with routing gates is designed to determine optimal computational paths, enabling data-dependent feature fusion. Additionally, bilinear attention mechanisms are employed to enhance feature interactions in spatial and channel representations. Extensive experiments on three public HSI and LiDAR datasets demonstrate the superiority of DCMNet over state-of-the-art methods. Our code will be available at https://github.com/oucailab/DCMNet.
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