GraphGST: Graph Generative Structure-Aware Transformer for Hyperspectral Image Classification
GraphGST: Graph Generative Structure-Aware Transformer for Hyperspectral Image Classification
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DOI：
10.1109/tgrs.2023.3349076
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发表时间：
2024
影响因子：
8.2
通讯作者：
Mengying Jiang;Yuanchao Su;Lianru Gao;A. Plaza;Xile Zhao;Xu Sun;Guizhong Liu
中科院分区：
文献类型：
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作者：
Mengying Jiang;Yuanchao Su;Lianru Gao;A. Plaza;Xile Zhao;Xu Sun;Guizhong Liu
Transformer在深度学习（DL）研究中具有重要意义。节点嵌入（NE）和位置编码（PE）通常是Transformer中不可缺少的两个组件。前者可以从数据中挖掘隐藏的相关性，而后者可以存储节点之间的位置关系。最近，Transformer已被应用于高光谱图像（HSI）分类，因为该模型可以捕获长距离的依赖关系，以聚合的全局特征表示学习。在HSI中，相邻像素趋于均匀，而NE不识别像素的位置信息。因此，PE对于Transformers理解像素之间的位置关系至关重要。然而，在这一领域，大多数基于transformer的方法随机生成PE，而不考虑其物理意义，这导致弱表示。本文提出了一种新的图形生成结构感知的Transformer（GraphGST），以解决上述PE问题时，实现HSI分类。在我们的GraphGST中，建立了一个新的绝对PE（APE）来获取像素的绝对位置序列（APS），并集成到Transformer架构中。此外，自监督学习的生成机制，以实现跨视图对比学习（CL），旨在提高Transformer的表示学习。所提出的GraphGST模型可以捕获局部到全局的相关性，并且所提取的APS可以补充像素的光谱特征以辅助NE。几个实验与真实的HSIs进行评估我们的GraphGST的有效性。与其他最先进的（SOTA）方法相比，该方法表现出非常有竞争力的性能。我们的源代码将在以下链接https://github.com/yuanchaosu/TGRS-graphGST中提供。
Transformer holds significance in deep learning (DL) research. Node embedding (NE) and positional encoding (PE) are usually two indispensable components in a Transformer. The former can excavate hidden correlations from the data, while the latter can store locational relationships between nodes. Recently, the Transformer has been applied for hyperspectral image (HSI) classification because the model can capture long-range dependencies to aggregate global features for representation learning. In an HSI, adjacent pixels tend to be homogeneous, while the NE does not identify the positional information of pixels. Therefore, PE is crucial for Transformers to understand locational relationships between pixels. However, in this area, most Transformer-based methods randomly generate PEs without considering their physical meaning, which leads to weak representations. This article proposes a new graph generative structure-aware Transformer (GraphGST) to solve the above-mentioned PE problem when implementing HSI classification. In our GraphGST, a new absolute PE (APE) is established to acquire pixels’ absolute positional sequences (APSs) and is integrated into the Transformer architecture. Moreover, a generative mechanism with self-supervised learning is developed to achieve cross-view contrastive learning (CL), aiming to enhance the representation learning of the Transformer. The proposed GraphGST model can capture local-to-global correlations, and the extracted APSs can complement the spectral features of pixels to assist in NE. Several experiments with real HSIs are conducted to evaluate the effectiveness of our GraphGST. The proposed method demonstrates very competitive performance compared with other state-of-the-art (SOTA) approaches. Our source codes will be provided in the following link https://github.com/yuanchaosu/TGRS-graphGST.