GE X S, ZHENG M M.Study on classification of few-shot remote sensing images based on SER-GNN[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(5):144-151.

DOI:10.16186/j.cnki.1673-9787.2023060037

Received: 2023/06/16

Revised: 2023/11/02

Published:2025/07/23

Study on classification of few-shot remote sensing images based on SER-GNN

Ge Xiaosan1,2, Zheng Mengmeng1,2

1.School of Surveying and Mapping and Land Information Engineering， Henan Polytechnic University， Jiaozuo  454000， Henan， China；2.Key Laboratory of Mine Spatio-Temporal Information and Ecological Restoration， MNR， Henan Polytechnic University， Jiaozuo  454000， Henan， China

Abstract: Objectives However， metric learning-based approaches often suffer from unclear feature distribution in the feature space under few-shot settings.To address this issue， a few-shot remote sensing image classification model named SER-GNN （SENet attention residual neural network and graph neural networks） was proposed.  Methods The model utilized attention mechanisms to enhance feature extraction capabilities through residual networks. Remote sensing image data was first processed by the SER-GNN convolutional layer （composed of ResNet-12 and SENet） to extract image features and enhance attention to key regions. Then， the image information and category label encodings were embedded into the GNN layer of the SER-GNN model. Finally， the implicit relationships between image categories were inferred using a message passing algorithm to construct an adjacency network and complete the classification task.  Results Experiments showed that the model improved classification accuracy by 1.35%， 2.15%， and 1.3% on the UC Merced Land-Use， AID， and NWPU-RESISC45 datasets under the 5-way 1-shot setting， respectively； and by 2.15%， 5.65%， and 3.85% under the 5-way 5-shot setting， respectively. Moreover， through a transfer learning strategy， the model trained on the NWPU-RESISC45 dataset demonstrated superior generalization performance when applied to the AID and UC Merced Land-Use datasets. Conclusions The proposed SER-GNN model effectively combined the structural advantages of convolutional and graph neural networks. It achieved higher classification accuracy in few-shot remote sensing tasks and demonstrated better adaptability in transfer learning. The results suggested that the model could adapt well to new learning environments and provided a promising technical path and methodological reference for intelligent classification of remote sensing images under few-shot conditions.

Key words:image classification;few-shot learning;ResNet-12;graph neural network;node embedding