Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification

Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote sensing image scene classification is how to extract effective features from insufficient labeled data. To solve these issues, a multi-scale graph-based feature fusion (MGFF) model is proposed for few-shot remote sensing image scene classification. In the MGFF model, a graph-based feature construction model is developed to transform traditional image features into graph-based features, which aims to effectively represent the spatial relations among images. Then, a graph-based feature fusion model is proposed to integrate graph-based features of multiple scales, which aims to enhance sample discrimination based on different scale information. Experimental results on two public remote sensing datasets prove that the MGFF model can achieve superior accuracy than other few-shot scene classification approaches.

Automated summary

Few-shot remote sensing image scene classification is a challenging problem. This paper proposes a multi-scale graph-based feature fusion model, which effectively represents spatial relations and integrates different scale information, achieving accurate classification with limited labeled data.