.png){kind=icon}
返回
Hyperspectral Image Classification With Contrastive Graph Convolutional Network
.png){kind=icon}
OA
AI
DOI：10.1109/TGRS.2023.3240721.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, graph convolutional network (GCN) has been widely used in hyperspectral image (HSI) classification due to its satisfactory performance. However, the number of labeled pixels is very limited in HSI, and thus, the available supervision information is usually insufficient, which will inevitably degrade the representation ability of most existing GCN-based methods. To enhance the feature representation ability, in this article, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed contrastive GCN (ConGCN), for HSI classification. First, in order to mine sufficient supervision signals from spectral information, a semisupervised contrastive loss function is utilized to maximize the agreement between different views of the same node or the nodes from the same land cover category. Second, to extract the precious yet implicit spatial relations in HSI, a graph generative loss function is leveraged to explore supplementary supervision signals contained in the graph topology. In addition, an adaptive graph augmentation technique is designed to flexibly incorporate the spectral-spatial priors of HSI, which helps facilitate the subsequent contrastive representation learning. The extensive experimental results on six typical benchmark datasets firmly demonstrate the effectiveness of the proposed ConGCN in both qualitative and quantitative aspects.
Keyword:
Contrastive learning
graph augmentation
graph convolutional network (GCN)
hyperspectral image (HSI) classification
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
暂无机构信息
引用论文
Adaptive DropBlock-Enhanced Generative Adversarial Networks for Hyperspectral Image Classification
OAAI
.png){kind=icon}
OAAI分享收藏Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification用于高光谱图像分类的双交互式图卷积网络
PREAI
分享收藏Local Manifold Learning-Based k-Nearest-Neighbor for Hyperspectral Image Classification基于局部流形学习的k近邻高光谱图像分类
PREAI
分享收藏
查看更多内容