.png){kind=icon}
返回
Diversity-Connected Graph Convolutional Network for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3298848.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hyperspectral image (HSI) classification methods based on the graph convolutional network (GCN) have received more attention because they can handle irregular regions by graph encoding techniques. However, GCN-based HSI classification methods are highly sensitive to the quality of the graph structure. Its performance degrades in the case of underdeveloped graphs because it cannot excavate the intrinsic adjacency relationships. Thus, it is necessary to improve the quality of graph structure in GCN-based methods. In this article, a novel diversity-connected GCN (DCGCN) method is proposed to improve the quality of the graph structure for HSI classification, and its basic idea can be adopted by other GCN-based methods. First, the potential neighbors are excavated by performing topological extensions based on the given graph. The diversity of surrounding neighbors is maintained by adaptively smoothing operation via a global threshold value from Kullback-Leibler (KL) divergence to eliminate weak interclass connections caused by weakly spectral variability. Second, another key connectivity restriction is imposed on the diverse neighbors to further refine the ambiguous connections of hard samples aiming at removing strong interclass connections where the spectral information is heavily confounded. Finally, the DCGCN method is analyzed theoretically to demonstrate its low-pass filter property. The comprehensive experiments demonstrate the effectiveness of the proposed DCGCN method and the basic idea of the diversity-connected graph in terms of overall accuracy (OA), kappa coefficient (KC), and average accuracy (AA) indices.
Keyword:
Diverse neighbors
graph connectivity
graph convolutional network (GCN)
hyperspectral image (HSI) classification
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Lightweight Tensorized Neural Networks for Hyperspectral Image Classification用于高光谱图像分类的轻量级张量神经网络
.png){kind=icon}
PREAI
分享收藏
The changing epidemiology of paediatric brain tumours: a review from the Hospital for Sick Children
PREAI
分享收藏Density and Thermal Conductivity Measurements for Silicon Melt by Electromagnetic Levitation under a Static Magnetic Field在静态磁场下通过电磁悬浮测量硅熔体的密度和热导率
PREAI
分享收藏
查看更多内容