论文标题
自适应跨注意驱动的空间 - 光谱图卷积网络，用于高光谱图像分类
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification
论文作者
论文摘要
最近，已经开发出图形卷积网络（GCN）来探索像素之间的空间关系，从而达到高光谱图像（HSIS）的更好分类性能。但是，这些方法无法充分利用HSI数据中光谱频段之间的关系。因此，我们提出了由空间GCN（SA-GCN）子网络组成的自适应交叉注意力驱动的空间 - 光谱卷积网络（ACSS-GCN），光谱GCN（SE-GCN）子网和图形交叉分析融合模块（GCAFM）。具体而言，提出了SA-GCN和SE-GCN通过分别在空间像素之间和光谱带之间建模相关性来提取空间和光谱特征。然后，通过将注意机制集成到图形的信息聚集中，GCAFM（包括三个部分，即空间图注意块，光谱图注意块和融合块）的设计旨在融合空间和频谱特征并抑制SA-GCN和SEGCN中的噪声干扰。此外，引入了自适应图的想法，以通过训练过程中的背部传播探索最佳图。两个HSI数据集的实验表明，所提出的方法比其他分类方法更好的性能。
Recently, graph convolutional networks (GCNs) have been developed to explore spatial relationship between pixels, achieving better classification performance of hyperspectral images (HSIs). However, these methods fail to sufficiently leverage the relationship between spectral bands in HSI data. As such, we propose an adaptive cross-attention-driven spatial-spectral graph convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN) subnetwork, a spectral GCN (Se-GCN) subnetwork, and a graph cross-attention fusion module (GCAFM). Specifically, Sa-GCN and Se-GCN are proposed to extract the spatial and spectral features by modeling correlations between spatial pixels and between spectral bands, respectively. Then, by integrating attention mechanism into information aggregation of graph, the GCAFM, including three parts, i.e., spatial graph attention block, spectral graph attention block, and fusion block, is designed to fuse the spatial and spectral features and suppress noise interference in Sa-GCN and Se-GCN. Moreover, the idea of the adaptive graph is introduced to explore an optimal graph through back propagation during the training process. Experiments on two HSI data sets show that the proposed method achieves better performance than other classification methods.
