Multi-hop double-branch network for small sample hyperspectral image classification

ABSTRACT

Neural networks have been extensively utilized in the classification of hyperspectral images. The study introduces a novel dual-branch structured network named MGACN for hyperspectral image classification under the condition of small sample sizes. This model a multi-hop graph network (MHGCN) and a graph attention convolutional network (GACNN). Graph Convolutional Network (GCN) integrates multi-hop mechanism to obtain and aggregate information between long-distance non-neighbor nodes. The Graph Attention Network (GAT) combines 2D-CNN by incorporating a noise reduction mechanism, as well as position and channel attention mechanisms. The classification accuracy reaches 98.43%, 99.02%, and 98.55% on the Indian Pines, Salinas, and Pavia University datasets, which is significantly better than that of the comparison model.

KEYWORDS:

• Hyperspectral image classification
• graph neural networks
• multi-hop structures
• small samples
• self-attention

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The Indian Pines, Salinas and Pavia University dataset is available at Hyperspectral Remote Sensing Scenes – Grupo de Inteligencia Computacional (GIC) (ehu.eus).

Ethics in publishing

Informed consent: Informed consent was obtained from all individual participants included in the study.